The ILD-model showed a significantly higher Dice score , and IoU-score  compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences.The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model ( In recent years, there has been significant effort in utilizing the next-generation AI technology, coined deep learning, to learn from labeled magnetic resonance imaging (MRI) data7. One key advantage of AI-based radiology is the automatization and standardization of tedious and time-consuming tasks, most clearly exemplified in the tasks surrounding detection and segmentation10. Several deep learning approaches have successfully been developed and tested for automatic segmentation of gliomas13, thanks in part to the publicly available brain tumor segmentation (BraTS) dataset14. In recent years, studies have also shown the potential of AI-based segmentation in patient cohorts comprising tumor subtypes, such as brain metastases, which may pose a greater challenge in terms of segmentation performance given their wide range of sizes and multiplicity16. Delineation of initial metastatic lesion size and changes related to disease progression or response are key neuroradiology tasks17. Traditionally, the metrics used for assessing brain metastases are based on unidimensional measurements, and although the value of using volumetric measurements has been increasingly discussed, expert groups remain reluctant to endorse a universal requirement of volumetric criteria for assessing brain metastases. One concern often raised is that volumetric analysis, as performed manually by radiologist, adds cost and complexity, and is not available at all centers. Consequently, there has been a strong need to develop an accurate pipeline capable of automatic detection and segmentation of brain metastases. In a recent study, we trained a fully convolution neural network (CNN) for automatic detection and segmentation of brain metastases using multisequence MRI18. While our DL-approach showed high performance, the robustness and clinical utility needs to be challenged in order to fully understand its strengths and limitations. In fact, many AI-based segmentation studies are limited in terms of generalizability in that the algorithms are trained and tested on single-center patient cohorts. In some studies, the training-sets and test-sets are even limited to a single magnetic field strength, a single vendor, and/or a single scanner for data acquisition. A key step towards understanding the generalizability and clinical value of any deep neural network is by training and testing using real-world multicenter data. Another limitation of these AI-based segmentation networks is that they are trained on a distinct set of MRI contrasts, which limits the use of the networks to sites acquiring the same sequences. However, deep neural networks should be able to handle missing model inputs. To this end, this work tested an AI-based segmentation model, called input-level dropout (ILD), in which a neural network with an input dropout layer is trained on the full set of four distinct MRI sequences, as well as every possible subset of the MRI sequences. Hence, our proposed model can improve the generalizability of deep learning segmentation models by enabling inference at imaging sites missing MRI sequences used for training. This proposition was investigated by testing the trained ILD-model on a patient cohort acquired at a different site and missing one of the four MRI sequences used for training. To evaluate this network’s performance, a second neural network was trained and tested using state-of-the-art architecture without applying the input-level dropout strategy, i.e., only trained on the limited sequences corresponding to those in the test set. We hypothesize that the ILD-model will yield segmentation performance comparable to that of a state-of-the-art segmentation network, while at the same time being robust towards missing input data and allow it to generalize to multicenter MRI data.Advances in artificial intelligence (AI) are suggesting the possibility of new paradigms in healthcare and are particularly well-suited to be adopted by radiologistsThe total time used for training was approximately 20 h for both the ILD-model and the DeepLab V3 network. For processing a test case using the ILD-model, the forward pass on a system with two NVIDIA GTX 1080Ti GPUs took approximately 250 ms per slice.p = 0.620), respectively , and IoU-score , compared to the DeepLab V3 network . By applying a lesion-size limit of 10 mm3, the ILD-model demonstrated an average FP of 3.6/patient, also significantly lower that the DeepLab V3   regardless of missing input MRI sequences. The accuracy of the ILD-model in detecting metastatic voxels in the brain, as measured by the AUC, is equivalent to that of the state-of-the-art DeepLab V3 neural network trained on the specific subset of sequences in our test set. However, our results indicate that the proposed model is superior to the DeepLab V3 in terms of segmentation performance, as measured by the Dice score and IoU, while at the same time returning significantly fewer FP. Nevertheless, the number of FP reported by the ILD-model remains a challenge that needs to be addressedThe neural networks used in this study were based on the DeepLab V3 architecture, which is considered as one of the most robust neural networks for image-based semantic segmentation. The key difference of the DeepLab V3 compared with other relevant networks is its reliance on atrous (or dilated) convolutions. By using atrous convolutional layers, our network has a large receptive field, thereby incorporating greater spatial context. This approach may be key to enabling the network to identify local features as well as global contexts, i.e., identifying brain regions, which could enhance the network’s decision-making process on similar local features.18. No previous studies have evaluated the deep learning for brain metastasis detection using multicenter data. Other single-center studies, such as Liu et al.15 and Charron et al.16, have recently shown that deep neural networks can detect and segment brain metastases with high performance, reporting results comparable to that of the current study. The latter study also demonstrated that a deep neural network trained on multisequence MRI data outperformed single contrast networks.In our study, the networks’ performance was tested on multicenter data, representing an essential step towards understanding the generalizability and clinical value of the proposed neural network. In this sense, it represents a logical extension of our prior single-center study on this topic20 and can be acquired by fast spin-echo or gradient-echo techniques. The 3D T1-weighted gradient-echo sequences  are broadly used because they create isotropic T1-weighted images with excellent gray-white matter differentiation, but are limited by lower contrast conspicuity and a lack of blood vessel suppression. The 3D fast spin-echo techniques  are relatively newer techniques optimized for isotropic high-resolution 3D imaging of T1-weighted, T2-weighted, or FLAIR images, and have the advantage of blood vessel suppression. For this study, post-Gd T1-weighted 3D fast spin-echo, pre-Gd and post-Gd T1-weighted 3D axial IR-FSPGR, and 3D FLAIR sequences were used as input to train the neural network. While these sequences are widely used for imaging brain metastases, they are not compulsory. Variations in sequences and acquisition parameters among different institutions frequently are present. For instance, 2D FLAIR (with thicker slice and non-isotropic voxels) may be acquired instead of 3D FLAIR. In clinical practice, it is also not unusual to omit sequences owing to patients’ safety or comfort. Therefore, it is imperative to design a robust and versatile neural network that can accommodate missing sequences while maintaining good performance. To this end, the goal of this study was to develop a deep learning model that is able to detect and segment brain metastasis with high accuracy, equivalent to that of the state-of-the-art DeepLab V3 model, even when the clinical end-user does not have access to all MRI data on which the model was trained. This is a major improvement for the generalizability of deep learning segmentation tools since many clinical sites do not the time or hardware to perform all four MRI scans.In general, the two most used MRI sequences for assessing brain metastases are post-Gd T1-weighted and T2-weighted FLAIR. The post-Gd 3D T1-weighted, high-resolution isotropic sequence is most crucial18. However, a total of nine patients in the current test set presented with >3 small metastases, for which the ILD-model still demonstrated a high accuracy and performance, equivalent to the average metrics for all lesions.While this study shows a high accuracy using the ILD-model for detecting and segmenting brain metastases, the results should be interpreted in light of the limited sample size and the homogeneity of the test cohort. Patients included in the test set were all scheduled for SRS, which generally presents with fewer and larger metastases, which in turn may be easier for the network to predict. This hypothesis is supported by observations made in our previous study, in which the tested neural network showed higher accuracy in patients with three or less metastases compared to patients with >3 metastasesIn conclusion, this study demonstrates that the ILD-model, utilizing a pulse sequence level dropout layer, thus being trained on all possible combinations of multiple MRI sequences, can detect and segment brain metastases with high accuracy, even when the test cohort is missing MRI data. This is likely of value for generalizing deep learning models for use in multiple different imaging sites.This retrospective, multicenter study was approved by the Oslo University Hospital and Stanford Review Board. The patient cohort consisted of a total of 165 patients with brain metastases, enrolled from two different hospitals, hereinafter referred to as “Hospital A” and “Hospital B”. From Hospital A, MRI data from a total of 100 patients were acquired and used for neural network training. These patients received their scans for clinical purposes, and our Review Board waived the requirement for informed consent. Further, a total of 65 patients from Hospital B were used for validation and testing, and written informed consent was obtained from all the patients.Inclusion criteria for the training data included the presence of known or possible metastatic disease , no prior surgical or radiation therapy, and the availability of all required MR imaging sequences (see below). Only patients with ≥1 metastatic lesion were included. Mild patient motion was not an exclusion criterion. For the validation and test data, we used MRI data from an ongoing clinical study (NCT03458455) conducted at Hospital B. To be eligible for inclusion, patients had to receive stereotactic radiosurgery (SRS) for at least one brain metastasis measured at a minimum of 5 mm in one direction, be untreated or progressive after systemic or local therapy, have confirmed non-small-cell lung cancer (NSCLC) or malignant melanoma, be ≥18 years of age; have an Eastern Cooperative Oncology Group performance status score ≤1, and have a life expectancy >6 weeks. Details on the patient cohorts are shown in Table n = 7; TwinSpeed and SIGNA Explorer, GE Healthcare, Chicago, USA) and 3 T  scanners. The training set included four MRI sequences; post-Gd T1-weighted 3D axial inversion recovery prepped fast spoiled gradient-echo (IR-FSPGR) (BRAVO/MPRAGE), pre- and post-Gd T1-weighted 3D fast spin echo (CUBE/SPACE), and 3D CUBE/SPACE fluid-attenuated inversion recovery (FLAIR). A dose of 0.1 mmol/kg body weight of gadobenate dimeglumine  was intravenously injected for Gd-enhancement. For the test set (n = 65), imaging was performed on a 3 T Skyra scanner , and included three MRI sequences; pre-Gd and post-Gd T1-weighted 3D fast spin echo (SPACE) and 3D T2-weighted FLAIR. Note that the 3D T1 BRAVO sequence is missing from the test set. All sequences with key imaging parameters are summarized in Table MRI training data was acquired on both 1.5 T  over each image slice where a lesion was visible on the Gd-enhanced IR-FSPGR sequence. The combined ROIs for a specific lesion comprised the volume of interest (VOI). In a separate session one week later, the second neuroradiologist with 9 years of experience confirmed (and modified as appropriate) final VOI placement for each lesion. All lesions were outlined using the OsiriX MD software package .For the test data, ground truth segmentations of Gd-enhancing metastatic lesions were manually drawn on post-Gd 3D T1-weighted spin echo data by two radiologists with 14 and 5 years of relevant experience. Delineations were performed using the nordicICE software package .All image-series were co-registered to a common anatomical space. For the training data, pre-Gd and post-Gd 3D T1-weighted spin echo data and FLAIR were co-registered to the post-Gd 3D T1-weighted IR-FSPGR, whereas for the test data, the post-Gd 3D T1-weighted spin echo images was used as reference for the pre-Gd 3D T1-weighted spin echo data and FLAIR. Prior to network training, a defacing procedure was applied to anonymize all imaging data using an in-house algorithm .21, and the modifications and training strategies are detailed in a recent work22. This study utilized and trained a “input-level integration dropout” network, referred to as the ILD-model .All patients from Hospital A were used for training, while the patients from Hospital B were divided into validation-sets and test-sets, giving a final breakdown of 100 training cases, 10 validation cases, and 55 test cases. All training was done on a system with two NVIDIA GTX 1080Ti GPUs.3 (roughly 2 mm in linear dimension) as a detected lesion.In this study, we perform statistical analysis on a voxel-by-voxel and lesion-by-lesion basis. The voxel-based approach allows investigation of differences in the probability maps at the smallest volume-level of the MR image-series. ROC curve statistics was used to evaluate the networks’ ability to differentiate between healthy and metastatic tissue on a voxel-by-voxel basis. For each patient in the test set, the area under the ROC curve (AUC) was measured. Further, the optimal probability threshold for including a voxel within the metastatic lesion was determined using the Youden index from the ROC statistics on the validation set. Using this threshold, the networks segmentation performance was further evaluated by estimating the precision-values and recall-values, false positive rate (FPR), as well as the IoU and Dice similarity score. The networks’ performance was also evaluated on a per-lesion basis by calculating the number of false positive (FP) per case. This metric was determined by multiplying the ground truth maps and the thresholded probability maps and counting the number of overlapping objects in the resulting binary image. By using a connecting component approach, voxels were considered connected if their edges or corner touch. The number of FP was determined both without any size criterion, as well as only considering objects ≥10 mmp-value of 5% or lower was considered to be statistically significant. All Statistical analyses were performed using MATLAB R2017a version 9.2.0 .Finally, the performance of the ILD-model and the DeepLab V3 network was compared using the Wilcoxon rank sum test. A Further information on research design is available in the Reporting Summary


The diagnosis of abusive head trauma (AHT) is complex and neuroimaging plays a crucial role. Our goal was to determine whether non-neuroradiologists with standard neuroradiology knowledge perform as well as neuroradiologists with experience in pediatric neuroimaging in interpreting MRI in cases of presumptive AHT (pAHT).blindly, i.e., no clinical history was given to the 3 non-neuroradiologists and 3 neuroradiologists from 2 different institutions.Twenty children were retrospectively evaluated. Patients had been diagnosed with pAHT (6 patients), non-abusive head trauma-NAHT (5 patients), metabolic diseases (3 patients), and benign enlargement of the subarachnoid spaces (BESS) (6 patients). The MRI was assessed Blindly, neuroradiologists demonstrated higher levels of sensitivity and positive predictive value in the diagnosis of pAHT (89%) than non-neuroradiologists (50%). Neuroradiologists chose correctly pAHT as the most probable diagnosis 16 out of 18 times; in contrast, non-neuroradiologists only chose 9 out of 18 times. In our series, the foremost important misdiagnosis for pAHT was NAHT (neuroradiologists twice and non-neuroradiologists 5 times). Only victims of motor vehicle accidents were blindly misdiagnosed as pAHT. No usual household NAHT was not misdiagnosed as pAHT. Neuroradiologists correctly ruled out pAHT in all cases of metabolic diseases and BESS.MRI in cases of suspected AHT should be evaluated by neuroradiologists with experience in pediatric neuroimaging. Neuroradiologists looked beyond the subdural hemorrhage (SDH) and were more precise in the assessment of pAHT and its differential diagnosis than non-neuroradiologists were. It seems that non-neuroradiologists mainly assess whether or not a pAHT is present depending on the presence or absence of SDH. The published review of the literature on “abusive head trauma” (AHT) in infants by the Swedish Agency for Health Technology and Assessment of Social Services (SBU)  triggereclinical experience, after an extensive multidisciplinary workup, suggests that when a young infant presents with hypoxic brain injury (HBI), SDH, and retinal hemorrhage, AHT is the most likely cause. Nevertheless, an experienced pediatric neuroradiologist will not only evaluate the triad but also other findings, such as the presence of ruptured and thrombosed cortical veins, ligamental injuries, myelination, or specifically the location and timing of the SDHs, as well as the age of the different brain injuries.The diagnosis of presumed AHT (pAHT) is complex and should always be the result of an extensive multidisciplinary approach. Nonetheless, neuroimaging plays a crucial role. Even though there are controversies; the main way for a radiologist to blindly assess pAHT is to evaluate the presence of the classical “triad” on magnetic resonance imaging (MRI): SDH, retinal hemorrhage (if possible), and parenchymal injuries. Enormous amounts of It is important for non-neuroradiologists and neuroradiologists to know that shaking is a contributor to AHT and that the old terminology “shaken baby syndrome” is a subset of AHT, i.e., AHT without impact. AHT is a well-recognized brain injury caused by the directed application of force to an infant or young child , 4. CaseThis retrospective study was approved by the ethics committee of Frankfurt University Hospital. “Consent” for the study was waived by the INSTITUTIONAL REVIEW BOARD/INDEPENDENT ETHICS COMMITTEE (IRB/IEC) of the Frankfurt University.The patient data relate to a 10-year period, from January 2008 to December 2017. Cases were selected, rather than from random search, by the neuro-pediatrician who heads the task force for child abuse cases. The minimum interval between imaging and reassessment of a case in the study was more than 1 year. Thereby, we avoided that the neuroradiologists initially involved in the clinical diagnosis could remember the case and thus be influenced in their decisions. In addition, non-neuroradiologists and neuroradiologists from 2 institutions evaluated the MRI images.Following pre-established inclusion and exclusion criteria  were included. Patients had been diagnosed with pAHT (6 patients), non-abusive head trauma (NAHT) (5 patients), metabolic diseases , and benign enlargement of the subarachnoid spaces (BESS) (6 patients).The general mean age was 7.7 months (newborn to 36 months) with an age-appropriate grouping and an equivalent mean age of 7.7 months in the group of pAHT. At 12.7 months, the mean age of NAHT was higher than that of pAHT, but the difference between these two groups was not significant. The mean age of the other groups was also comparable at 6.3 and 4.4 months.Cases were selected by the neuro-pediatrician who is responsible for child abuse cases. All cases were discussed in a multidisciplinary team consisting at least of a neuro-pediatrician, a neuroradiologist, a pediatric radiologist, a pediatric ophthalmologist, a metabolic, and a forensic doctor. After analyzing all clinical findings, performing the recommended procedures according to the current S3 AWMF*-Guideline of 2019  and ruliInfants with pAHT presented with symptoms of their injury and the clinical history were incomplete, inconsistent, or incorrect. The following presentations were observed: 3 cases of a fall from a low height (≤ 0.5 m); 1 child in good general health with associated progressive macrocephaly; 1 with hydrocephalus; and 1 who presented at the emergency room without previous trauma.An obligatory inclusion criterion was the presence of retinal bleeding. These had to be diagnosed by the ophthalmologist, be present on both sides, and affect several layers of the retina. Retinal hemorrhage was a crucial criterion that was collected independently of neuroradiological imaging.MRI positive concomitant brain and/or ophthalmological injuries, such as SDH(s), parenchymal injuries, cerebral edema, avulsed and thrombosed cortical veins, and retinal hemorrhages.In addition to retinal hemorrhage and inadequate or missing clinical history, patients had Metabolic diseases were ruled out.In addition, spinal, skin, and skeletal injuries were evaluated but not shown to radiologists.The goal was to include various severe degrees of pAHT. Clinically, patients presented from no symptoms to coma requiring resuscitation. One case of pAHT with impact was also included in the study. Cases were diagnosed as pAHT according to the following criteria:To note for all cases of pAHT, we used blinded data, detailed reporting on the exclusion of differential diagnoses  . We consAHT with impact (1 patient).The pAHT cases (6 patients) were sub-classified as follows: (A) pAHT without impact with bilateral SDHs (3 patients)—MRI showed avulsed/thrombosed cortical veins and possible retinal hemorrhage; (B) massive pAHT without impact with cerebral edema (2 patients)—MRI showed diffusion-weighted image (DWI) lesions, avulsed and thrombosed cortical veins , parenchymal hemorrhage, and possible retinal hemorrhage in MRI (1 patient); and (C) pThe main criterion for inclusion was an intracranial hemorrhage (SDH and/or subarachnoid hemorrhage (SAH)), which, together with all other clinical abnormalities, was compatible with the details of the described accident  spaces and a disorder of myelination. One patient had glutaric aciduria, type 1; his/her MRI showed atrophy of the frontotemporal regions with enlarged subarachnoid spaces and delayed myelination. One had a cobalamin defect and one methylmalonic aciduria, both with an enlargement of the external CSF spaces and borderline delayed myelination.In addition to the neuroradiological evidence of dilation of the external CSF, it was important from an inclusion perspective that the patients had no neurological symptoms and that the enlargement of the subarachnoid space and the head circumference receded in the follow-up. In addition, myelination was correct for age. Six patient cases were included in this group. One of these had, in addition to the enlargement of the external CSF space, a unilateral, homogeneous, subacute subdural hematoma, which was most likely based on a minor trauma after the exclusion of coagulation disorders and metabolic disorders. There were no indications of a pAHT. The neuroradiological examination was initiated in this case to clarify macrocephaly.The evaluation was performed within 2 university hospitals and included 6 examiners from 2 centers. 3 senior neuroradiologists -2 with additional pediatric neuroradiology experience, one with over 10 and the other 20 years of neuroradiology expertise; 1 pediatric neuroradiologist with over 20 years of neuroradiology expertise. 3 non-neuroradiologists with basic neuroradiology knowledge  axial, T1- and T2-weighted axial images, DWI/apparent diffusion coefficient (ADC) maps, axial and coronal, and T2 sequence of a multi-echo gradient recalled echo (GRE) in the axial plane. All MRIs were performed a 1.5 T MRI. The age of the patients at the time of the examination was indicated. Specifying the age was important to assess myelination. The radiologists were not made aware of the follow-up, other imaging studies, or their results.Evaluation of the MRI was performed in two rounds. In the first, the radiologist had to give a definitive yes/no answer to pAHT or select “pAHT could not be excluded.” In the case of a “no to pAHT” or “pAHT could not be excluded,” they should decide what other diagnoses  they would consider. For the first evaluation round, investigators had to review the cases without the help of a questionnaire or clinical history.For the second, detailed guidance of possible MRI findings in pAHT was included to help guide diagnosis, and the physician was then allowed to reconsider the original diagnosis. Again, clinical history was omitted.Only the results of the first round are provided in this study, in which the investigators judged the cases alone, according to their knowledge. Our goal was to avoid the influence of specific questions . In contrast, the second round was added to specifically evaluate if guidance would help.The statistical evaluation of the collected data was carried out with the help of “Microsoft Excel” and the university’s own statistics program “Bias.” We were supported by the Institute for Biostatistics and Mathematical Modelling of Frankfurt University Hospital.To determine the role of an MRI examination in cases of pAHT, the sensitivities and positive predictive value were determined individually for each examiner and the respective mean values for the groups of examiners. The results were then compared. The basis for the calculation was the answers from the first assessment round. To determine whether the results improved by presenting a structured answer sheet, we compared the sensitivities and positive predictive values of the first and second assessments.We determined the consistency of the assessment within the two groups of examiners by determining Fleiss’ kappa coefficient for both groups of examiners. Here, the answers of the first assessment were relevant, in which the investigators judged the cases alone and had to determine the most probable diagnosis.We also assessed patients individually and described specifically which cases misjudgments most commonly occurred.One hundred twenty questionnaires were evaluated in both the first and second rounds.Neuroradiologists showed a strong agreement in their case assessments Graphic  and moreThe 3 neuroradiologists chose correctly pAHT as the most probable diagnosis in 16 out of 18 times. One case of pAHT with impact . Results deteriorated even further after detailed guidance of possible MRI findings was provided. Presumed AHT was assessed as NAHT seven times and twice as a metabolic disease.Overall, neuroradiologists were blindly able to determine whether a pAHT was present or if it could be excluded Graphics  and 2 moThe sensitivity in the diagnosis of pAHT, i.e., correctly choosing pAHT as the most probable diagnosis, was 89% for neuroradiologists and 50% for non-neuroradiologists. Results for positive predictive value, i.e., the probability of patients who have a positive MRI result actually fitting the hypothesis pAHT, were identical to the results for sensitivity.It was more difficult for non-neuroradiologists to diagnosis pAHT in children who presented with SDH but no parenchymal injury. In comparison, neuroradiologists reliably chose correctly pAHT as the most probable diagnosis in patients with SDH but without parenchymal injury; they correctly recognized the retinal bleeding and detected bridging vein injury associated with the SDH. None of the non-neuroradiologists recognized or suspected retinal bleeding.In two cases, pAHT presented with brain edema. These cases were more often misdiagnosed. One case  pAHT.In 3 patients  and one who suffered a fall from a low height (NAHT5), pAHT was ruled out by all investigators.In all cases of metabolic diseases, the 3 neuroradiologists were able to correctly rule out pAHT. The non-neuroradiologists correctly ruled out pAHT in 8 out of 9 case assessments. In 1 case , pAHT was wrongly assumed to be the most probable diagnosis. All other investigators were able to assign this case to a metabolic genesis.All neuroradiologists assessed myelination as delayed in patients with metabolic diseases. Remarkably, the 2 non-neuroradiologists who correctly suspected metabolic disease did not detect any disturbance of myelination.Neuroradiologists correctly ruled out pAHT in all cases of BESS. Non-neuroradiologists ruled out pAHT correctly in 14 out of 18 evaluations. In 3 case assessments, pAHT was wrongly assumed to be the most probable diagnosis and in 1 evaluation pAHT could not be ruled out. One case with a widening of the external cerebrospinal fluid spaces and associated unilateral SDH proved to be, as expected, difficult for non-neuroradiologists Fig. .Legal cases of AHT have become more complex during the last 20 years . GuideliThe radiologist who analyzes the brain MRI performed after pAHT plays a key role in its diagnosis and differential diagnosis. Neuroradiologists, who are routinely exposed to pediatric cases, have learned after years of training to analyze the pediatric brain MR images systematically and to interpret all findings. This perception is different in a non-neuroradiologist without advanced neuroradiology training.Our series showed that, blindly, it was only possible to distinguish pAHT from other differential diagnoses with high confidence (sensitivity of 88.89%) if the assessment of brain MRI images was carried out by a neuroradiologist with experience in neuropediatrics. This result is of particular interest in cases of unwitnessed abuse, where the brain MRI can provide crucial information with which to initiate further diagnostics. In this study, we deliberately avoided providing information about the clinical situation of the patients to the evaluator.The level of experience of radiologists plays an especially important role when pAHT does not present itself with bilateral SDH, which is the most frequent intracranial lesion in patients with AHT . Non-neuWhen compared with non-neuroradiologists, neuroradiologists based their decisions on different imaging aspects and not only on the presence or absence of SDH. They were therefore able to differentiate the cases more precisely. Even if SDHs were present, a pAHT could be excluded.The presence of parenchymal brain injury in patients with AHT is the most significant cause of morbidity and mortality . ContusiRetinal bleeding is classically associated with AHT, but also it has other causes, such as accidental trauma. In cases of NAHT, the retina shows fewer bleeding (except in cases of severe trauma), which is usually limited to the posterior pole . It is eIn our data, severe NAHT was more likely to be mistaken for pAHT. It is important to emphasize that only the cases of severe MVA with bilateral SDHs , which is the common misleading history provided by caregivers.Neuroradiologists blindly detected 16 out of 18 evaluations of pAHT, and all cases of pAHT were correctly detected blindly by at least 2 of the 3 neuroradiologists. Furthermore, the neuroradiologists also succeeded in identifying cases of pAHT in which no or only minor SDHs were present. They were able to detect retinal bleeding more precisely in the MRI and interpret it correctly.In contrast, non-neuroradiologists chose correctly pAHT as the most probable diagnosis only in 50% of the pAHT case assessments correctly . In 7 evaluations, pAHT was interpreted as NAHT and twice as a metabolic disease. The second round, where we tried to improve the results with specific questions such as asking about retinal bleeding, resulted in even worse results for the non-neuroradiologist group. This could be explained by the fact that compared with neuroradiologists, non-neuroradiologists are rarely exposed to such specific images, and combining specific findings into a coherent, organized way of diagnosing pAHT, is difficult.In cases of pAHT, neuroradiologists more often suspected or detected retinal bleeding. This shows that a higher degree of experience is required to detect retinal bleeding in MRI. Teixeira et al.  showed tCerebral edema can result from abusive and non-abusive trauma or from other nontraumatic causes such as meningitis or encephalitis (infections were not included). Specifically, due to the limited cerebral autoregulation, AHT is often associated with hypoxia and brain edema . This is in agreement with the previous literature , 19–25. In our sample, as to be expected under blind conditions, the correct recognition of a brain fracture misled the diagnosis. One patient . In these cases, the injuries and MRI findings are proportional to the height. Couches, for example, have been blamed for injuries. The reality is that children almost never die in falls from low heights and they rarely even suffer serious injuries. The specific case of a fall from a height of 0.5 m (case “NAHT5”) was identified as NAHT by all examiners, who recognized the presence of soft tissue swelling and assigned the case as accidental injury.In an extensive review of the literature, Choudhary et al.  showed tAnother important differential diagnosis is birth traumatic injury (“NAHT4”). In this case, all neuroradiologists and non-neuroradiologists were able to rule out the possibility of a pAHT and correctly assumed an accidental genesis. Hemorrhage can occur after vaginal delivery or cesarean and presents as an asymptomatic thin subdural or intradural collection (< 3 mm) located posteriorly. It resolves within a month to 6 weeks and does not appear to rebleed . The SDHIt is important to emphasize the radiological distinction between pAHT and NAHT. The accidental trauma presents with extra-axial hemorrhages close to the site of injury with soft tissue swelling. An epidural hematoma happens more often in cases of NAHT, but it does not rule out pAHT; in children, it is often caused by a direct traumatic violent effect. In contrast to adults, epidural hemorrhage in children is predominantly of venous origin, mostly due to injury of the sinus or the diploic veins in children with skull fractures , 28. In In contrast, SDH in children with AHT is usually multifocal, typically along the posterior interhemispheric fissure, close to the vertex, due to the rupture of the bridging veins Fig.  and 4.FiSubarachnoid, intraparenchymal, and intraventricular hemorrhage are non-specific and occur equally in AHT and NAHT , 22–25.The presence of ruptured bridging veins is not pathognomonic for AHT, but its presence supports a traumatic cause for the SDH . TypicalThe second most common misdiagnosis within the pAHT group was a metabolic disease (2 out 18 case assessments of pAHT in the first round by non-neuroradiologists but none by neuroradiologists).Our raters had no access to clinical information or to the head circumference, both of which play an important role in the differential diagnosis . An acutThe neuroradiologists paid attention to the myelination stage, while none of the non-neuroradiologists did. In addition, the neuroradiologists were aware that infants with metabolic disease, such as glutaric aciduria type I (GA1), are predisposed to develop SDH. The relatively high estimated incidence of SDH (20–30%)  in childThe evaluation of normal myelination is important. In this pilot project, no metabolic disease or BESS was misdiagnosed as pAHT by neuroradiologists. BESS is a common finding in imaging studies indicated by macrocephaly in infancy and the recognition thereof is important. No misdiagnosis was made by neuroradiologists. In comparison, non-neuroradiologists had difficulties in correctly ruling out pAHT in cases of BESS, especially when it was associated with SDH.Differentiating between SDHs and enlargement of the subarachnoid spaces is one of the most important responsibilities when interpreting brain imaging in infants. The distinguishing feature by which BESS and SDH can be differentiated is the location of the bridge veins Fig.  9]. In . In 9]. It is questionable if BESS predisposes SDH. According to a recent review , only arIt appears that non-neuroradiologists assess whether or not a pAHT is present mostly depending on “the presence of subdural hematomas.” The presence or suspicion of retinal bleeding played a minor or no role in the evaluation of the non-neuroradiologists.Neuroradiologists as a rule looked beyond SDH and were consequently more precise in assessing pAHT and its differential diagnosis than non-neuroradiologists. Basically, neuroradiologists with pediatric expertise make the diagnosis of pAHT not only based on the “triad”  but also on other findings, such as ruptured and thrombosed cortical veins, the presence of SDHs in different locations, and age and/or parenchymal injuries with different ages. In addition, they routinely evaluate myelination. All neuroradiologists were able to rule out all cases of birth trauma, a fall from a low height, BESS, and metabolic diseases.In cases of traffic accidents and NAHT, the differential diagnosis with pAHT was, blindly, particularly difficult, even for neuroradiologists, due to the differential diagnosis with pAHT associated with impact. But after a MVA, medical history would not give any room for such misdiagnosis.Despite extensive literature in neuroimaging of AHT, the early presumption of AHT remains a challenge not only for non-neuroradiologists but also for neuroradiologists. The final diagnosis of presumptive AHT can never be based only on radiological findings and should never be based on a single injury. Presumptive AHT should be viewed as a combination of all imaging findings, including extracranial injuries, in the overall clinical context. Other diseases which cause similar brain MRI findings should be ruled out. In addition, the diagnosis should be made inter-disciplinary, based on medical, forensic, and social investigation.The results of our study should be viewed critically due to the large confidence intervals. It was a pilot project and can be used as the basis for future data collection in a multicenter prospective study.


Silver-Russell syndrome (SRS) is a representative ID in SGA-SS and has heterogenous (epi)genetic causes.To clarify the contribution of IDs to SGA-SS and the molecular and phenotypic spectrum of SRS, we recruited 269 patients with SGA-SS, consisting of 103 and 166 patients referred to us for genetic testing for SGA-SS and SRS, respectively. After excluding 20 patients with structural abnormalities detected by comparative genomic hybridization analysis using catalog array, 249 patients were classified into 3 subgroups based on the Netchine-Harbison clinical scoring system (NH-CSS), SRS diagnostic criteria. We screened various IDs by methylation analysis for differentially methylated regions (DMRs) related to known IDs. We also performed clinical analysis.H19/IGF2:intergenic-DMR and uniparental disomy chromosome 7, being major genetic causes of SRS, was detected in 30.4% of patients in the “SRS-compatible group” and in 13.8% of patients in the “non-SRS group.”These 249 patients with SGA-SS were classified into the “SRS-compatible group” (n = 148), the “non-SRS with normocephaly or relative macrocephaly at birth group” (non-SRS group) (n = 94), or the “non-SRS with relative microcephaly at birth group”  (n = 7). The 44.6% of patients in the “SRS-compatible group,” 21.3% of patients in the “non-SRS group,” and 14.3% in the “non-SRS with microcephaly group” had various IDs. Loss of methylation of the We clarified the contribution of IDs as (epi)genetic causes of SGA-SS and the molecular and phenotypic spectrum of SRS. Various IDs constitute underlying factors for SGA-SS, including SRS. Approximately 10% of babies born small-for-gestational-age (SGA), a condition for babies with birth weight (BW) and/or birth length (BL) less than those expected for the gestational age and sex, do not have catch-up growth until 2 years of age . The cau(Epi)genetic disorders causing SGA-SS include genetic syndromes caused by defects in genes associated with human growth, imprinting disorders (IDs), and pathogenic copy number variants (PCNVs). Among them, IDs are caused by abnormal gene expression of the imprinted genes. Imprinted genes are expressed in a parental-origin-specific manner and are associated with fetal and placental growth . ParentaH19/IGF2:intergenic (IG)-DMR (H19LOM) and maternal uniparental disomy chromosome 7 (UPD(7)mat) (H19LOM and UPD(7)mat did not satisfy NH-CSS criteria, and were diagnosed as SGA-SS (H19LOM and UPD(7)mat met NH-CSS criteria and were diagnosed as SRS  is the clinical diagnostic criteria for SRS having the most sensitive and the highest negative predictive value . A conseD(7)mat) ; howevers SGA-SS . Moreoved as SRS , 14.Previous work has examined the contribution of IDs to SGA-SS; however, the number of patients involved was not so large, and clinical assessment using clinical diagnostic criteria for SRS was not performed . To clarThis study was approved by the Institutional Review Board Committee at the National Center for Child Health and Development and performed after obtaining written informed consent from patients or patients’ parents to participate in this study and for publication of the clinical and molecular information.From 2002 to 2019 we recruited 220 patients referred to us for investigation for SGA-SS and 431 patients referred to us for genetic testing for SRS . Among thttp://dgv.tcag.ca/dgv/app/home) and previous reports. Eventually, when CNVs were detected in our study involved in the regions causing known microdeletion or microduplication syndromes, we defined those CNVs as PCNVs. Next, to detect IDs, we performed methylation analysis with pyrosequencing for 9 DMRs related to known IDs, namely, the H19/IGF2:IG-DMR on chromosome 11, PEG10:transcription start site (TSS)-DMR and MEST:alt-TSS-DMR on chromosome 7, PLAGL1:alt-TSS-DMR on chromosome 6, KCNQ1OT1:TSS-DMR chromosome 11, MEG3/DLK1:IG-DMR and MEG3:TSS-DMR on chromosome 14, SNURF:TSS-DMR on chromosome 15, and GNAS-A/B:TSS-DMR on chromosome 20, as previously reported mat (H19LOM and UPD(7)mat, such as TS14, PWS, UPD(6)mat, UPD(16)mat, and UPD(20)mat, we conducted structural analysis to detect micro-deletions and/or duplications involving the imprinted regions. For structural analysis, we used methylation-specific multiplex ligation-dependent probe amplification  analysis for each imprinted region  and/or aCGH analysis using custom built array  and/or genome-wide comparative genomic hybridization and single-nucleotide polymorphism array , according to the manufacturer’s instructions. For patients with abnormal methylation levels of the DMRs related to known IDs other than H19LOM and UPD(7)mat, but not structural abnormalities, we carried out microsatellite analysis for chromosomes 6  to detect PCNVs. To access the PCNVs, we found copy number variants (CNV) that had 3 or more continuous probes with aberrant signals. Then we evaluated whether these CNVs were pathogenic or not by referring to the Database of Genomic Variants http://dgH19LOM and UPD(7)mat, which are major genetic causes of SRS, were detected in 58 patients (23.3%), and IDs other than H19LOM and UPD(7)mat were in 29 patients (11.6%). Twenty patients with IDs other than H19LOM and UPD(7)mat were previously reported (see H19LOM and UPD(7)mat were identified in 30.4%, 13.8%, and none of patients in the SRS-compatible group, the non-SRS group, and the non-SRS with microcephaly group, respectively. IDs other than H19LOM and UPD(7)mat were identified in 14.2% of patients in the SRS-compatible group, in 7.4% of patients in the non-SRS group, and in 14.3% of patients in the non-SRS with microcephaly group. Particularly, 8 patients with TS14 were included in the SRS-compatible group. The non-SRS with microcephaly group had the lowest detection rate (14.3%) for IDs in patients.The results of molecular analyses are summarized in rted see   showed mild hypomethylation of the MEG3/DLK1:IG-DMR and MEG3:TSS-DMR besides H19LOM. Patient 2 with MLID (MLID 2) showed mild hypomethylation of the GNAS-A/B:TSS-DMR besides H19LOM. Patient 3 with MLID (MLID 3) showed mild H19LOM besides hypomethylation of the MEG3:TSS-DMR, as previously reported (Three patients (3.4%) of 87 patients with various IDs had MLID. Two patients showed obvious reported . A singlH19LOM, UPD(7)mat, IDs other than H19LOM and UPD(7)mat, and unknown etiology are summarized in H19LOM and UPD(7)mat, and 20 patients with IDs other than H19LOM and UPD(7)mat had clinically suspected SRS. Clinical manifestations in patients with each etiology are summarized in H19LOM and UPD(7)mat showed that patients with H19LOM had typical SRS phenotypes, such as severe prenatal and postnatal growth failure and higher frequency of the remaining clinical features in NH-CSS. BOFC of patients with H19LOM were more preserved than those of patients with other etiologies. Patients with UPD(7)mat showed less serious prenatal growth failure compared to patients with H19LOM, showed severe postnatal growth failure similar to patients with H19LOM, and had a high frequency of relative macrocephaly at birth (H19LOM and UPD(7)mat showed feeding difficulties and/or low BMI in over 70%. Of clinical features frequently observed in SRS patients, triangular face was observed in approximately 90% of patients with H19LOM and in 70% of patients with UPD(7)mat. Fifth finger clinodactyly was detected in more than 70% of patients with H19LOM and in about 45% of patients with IDs other than H19LOM and UPD(7)mat. Muscular hypotonia was observed at relatively high frequency in patients with IDs other than H19LOM and UPD(7)mat, compared to those with other etiologies. Speech delay was detected in 40% and 58.6% of patients with UPD(7)mat and IDs other than H19LOM and UPD(7)mat, respectively. Congenital heart diseases (CHDs) were observed in 16% of patients with IDs other than H19LOM and UPD(7)mat. Genitourinary anomalies were observed in approximately 30% of patients with H19LOM.Clinical data for each subgroup in patients with at birth . PatientAll 3 patients with MLID were classified into the SRS-compatible group. Thus, the frequency of MLID was 2.0% of patients in the SRS-compatible group and 4.5% of patients with various IDs in the SRS-compatible group. MLID 1 had 5 NH-CSS items other than body asymmetry, and also showed triangular face, fifth finger clinodactyly, cryptorchidism, hypospadias, and pelvic bone defect. His BW, BL, and present height were −4.2, −3.8, and −5.8 SDS, respectively. MLID 2 had 5 NH-CSS items other than feeding difficulties together with fifth finger clinodactyly. Her BW, BL, and present height were −3.5, −3.4, and −3.3 SDS, respectively. Clinical features of MLID 3 have been reported previously . In brieH19LOM and UPD(7)mat in H19LOM and UPD(7)mat, was diagnosed in 6 patients with maternal uniparental disomy chromosome 14 and 5 patients with epimutation at the 14q32.2 imprinted region. Eight patients with TS14 were classified into the SRS-compatible group. The etiology with the second largest number of patients was UPD(20)mat. All patients with UPD(20)mat were classified into the SRS-compatible group and the non-SRS group. UPD(6)mat, PWS, and UPD(16)mat were detected in several patients. In addition to chromosomal abnormalities involving the imprinted region, parthenogenesis was identified in a single patient. Muscular hypotonia was observed in all patients with PWS, and about half of patients with TS14, UPD(20)mat, 11p15 maternal duplication, and UPD(16)mat. Speech delay was observed in all patients with PWS and 11p15 maternal duplication, and in about half of patients with TS14, UPD(20)mat, and UPD(16)mat. Genitourinary anomalies were frequently identified in UPD(6)mat, PWS, and UPD(16)mat.We summarized clinical features of 29 patients with IDs other than H19LOM and UPD(7)mat. These results suggest that H19LOM and UPD(7)mat do not contribute to the development of SGA-SS with relative microcephaly. The frequency of H19LOM and UPD(7)mat in the SRS-compatible group in our study was lower than those (76.7%) in a previous study, which examined 60 patients with 4 or more NH-CSS items (H19LOM and UPD(7)mat were detected in 14.2% of patients in the SRS-compatible group, in 7.4% of patients in the non-SRS group, and 14.3% of patients in the non-SRS with microcephaly group. Particularly, TS14 and UPD(20)mat were detected in at least 5 patients. A previous study reported the results of methylation analysis for 11 imprinted loci  in 79 patients with growth restriction who were classified into 4 clinical referral categories, namely, SRS, intrauterine growth restriction (IUGR), short stature (SS), and IUGR and SS mat, IDs other than H19LOM and UPD(7)mat, and unknown etiology produced several notable clinical findings. First, 21 patients satisfying NH-CSS had etiologies other than H19LOM and UPD(7)mat, and meanwhile, 13 patients with H19LOM and UPD(7)mat did not satisfy NH-CSS. Consistent with this, Azzi et al reported a single patient with UPD(7)mat who did not satisfy NH-CSS (H19LOM and UPD(7)mat carriers do not always show the unambiguous SRS phenotype (H19LOM and UPD(7)mat having SGA-SS without typical SRS phenotype, some other factors such as environmental, maternal, paternal, placental, and other genetic factors may affect their phenotypes. Second, most patients with H19LOM, two-thirds of patients with UPD(7)mat, and two-thirds of patients patients with IDs other than H19LOM and UPD(7)mat satisfied NH-CSS. Patients with H19LOM showed the most severe prenatal and postnatal growth failure. In addition, relative macrocephaly, protruding forehead, and body asymmetry were frequently detected in patients with H19LOM. Patients with UPD(7)mat had heavier BW and taller BL than patients with H19LOM; however, patients with UPD(7)mat showed severe postnatal growth failure as well as those with H19LOM. Furthermore, 90% of patients with UPD(7)mat had relative macrocephaly, whereas only 9% had body asymmetry. These results indicate the differences in clinical features related to NH-CSS between patients with H19LOM and patients with UPD(7)mat, as previously reported (H19LOM and UPD(7)mat. This is probably because patients with TS14 and PWS, who have muscular hypotonia with high frequency (H19LOM and UPD(7)mat. Speech delay was observed in higher frequency in patients with UPD(7)mat, and those with IDs other than H19LOM and UPD(7)mat, than in those with H19LOM. In this regard, abnormal gene expression of FOXP2 on chromosome 7 associated with language development and dysfunction of the genes related to language development caused by IDs may lead to speech delay (H19LOM and UPD(7)mat. Previous studies also showed that some DNA methylation abnormalities were associated with CHD (H19LOM and UPD(7)mat. Genitourinary anomalies were observed in about 30% of patients with H19LOM. Several studies have reported that male genitourinary anomalies were frequently identified in SRS . IUGR has been reported as one of the most common features of UPD(6)mat; however, it is unknown whether SS and the remaining NH-CSS items are common features or not due to lack of clinical information about patients with UPD(6)mat mat mosaic or 11p15 maternal duplication showed SRS phenotype as well as previous reports . UPD(11)feration , 33, andwith SRS . Over 70with SRS . On the PD(6)mat . In our PD(6)mat , 38. FurIn our study, the frequency of MLID was 3.4% in the patients with various IDs, and 4.5% in patients with various IDs classified into the SRS-compatible group. Recently, Eggermann et al reported that the frequency of MLID was 3.6% in SRS patients with aberrant findings . These fThis study has some limitations. First, because our laboratory is one of the largest facilities that can perform genetic analyses of SRS in Japan, some selection bias for recruiting patients may occur, that is, patients with SGA-SS clinically diagnosed by attending physicians have some aspect of SRS phenotype. Second, many patients included in our study were preschool children under 3 years old. It is possible that developmental delay and characteristic features of each etiology were unclear at the time of examination. Lastly, we did not perform mutation screening of genes that cause genetic syndromes leading to SGA-SS. Patients with unknown etiology in our study may have had gene mutations of genetic syndromes resulting in SGA-SS.In conclusion, we clarified the contribution of IDs to SGA-SS. This study also showed broad molecular and phenotypic spectrums of SRS. Various IDs constitute underlying factors for SGA-SS, including SRS.


In recent years, several trials of breast cancer treatment have failed to demonstrate a survival benefit for some previously routine surgical therapies in selected patient groups. As each of these therapeutic approaches has been deemed of low value deimplementation has varied significantly. This demonstrates that effective de-escalation in breast cancer surgery relies on more than the availability of data from randomized controlled trials and other high-quality evidence, but is also influenced by various stakeholders, social expectations, and environmental contexts. Current national recommendations for surgical de-escalation in breast cancer care include the avoidance of completion axillary lymph node dissection (ALND) in patients with micrometastases or macrometastases in 1 or 2 sentinel nodes, re-excisions for close but negative surgical margins after partial mastectomy, contralateral prophylactic mastectomy in women with average-risk unilateral breast cancer, and sentinel lymph node biopsy (SLNB) in patients aged >70 years with early-stage hormone receptor–positive (HR + ) cancer  after neoadjuvant chemotherapy, and areas under study such as the omission of surgery altogether in selected women with DCIS or in women with clinical complete responses to chemotherapy. As each of these therapeutic approaches can be considered low value , their effective deimplementation remains influenced by various stakeholders, social expectations, and environmental contexts. By examining these deimplementation processes, we can identify factors contributing to the persistent use of low-value services and can leverage this knowledge to design strategies to reduce or eliminate them.Over last 20 years, a growing number of trials of breast cancer treatment demonstrate equivalent survival outcomes when some previously routine therapies, such as axillary dissection and radiotherapy, are omitted in selected patient groups. These findings have led to recommendations for surgical de-escalation by various multidisciplinary oncology groups5.Surgical procedures incurring costs and possible harms for patients without contributing to improved oncologic or survival outcomes are designated as low value. Harms extend beyond potential short-term surgical complications from the intervention itself to include potential downstream dangerous care cascades , increased travel burdens for patients and their families, financial toxicity through direct costs and patient-time costs to undergo treatments rather than earn income, and excess health care utilization and spending that may be more appropriately reallocated to high-value services6. Third, breast cancer care is largely decentralized from academic medical centers and is provided in the community, requiring systematic and complete dissemination of de-escalation recommendations. Finally, as breast cancer treatment is inherently multidisciplinary, effective de-escalation of low-value surgery requires the support and coordination of the entire treatment team.The management of early-stage breast cancer is ideal for understanding and improving efforts to de-escalate low-value treatments. First, breast cancer is common and well-studied, with many high-quality randomized controlled trials supporting guidelines for de-escalation. Second, broad uptake of screening mammography has resulted in a large cohort of patients with early-stage, highly curable disease who are at risk for overtreatmenthttps://www.choosingwisely.org). Choosing Wisely is an international campaign, initiated by the American Board of Internal Medicine Foundation, that has engaged more than 80 specialty organizations to issue 550-plus recommendations to avoid certain low-value tests or treatments. These guidelines are intended to inform consultations between physicians and patients to facilitate delivery of care that is supported by evidence and is both essential and appropriate. Prominent breast surgical oncology groups, including the Society of Surgical Oncology (SSO), the American Society of Breast Surgeons, and the American College of Surgeons, participate in the campaign and have identified numerous low-value practices. The recommendations specific to low-value breast surgical practices were first issued in 2016 and were updated in 2020; the trials that supported de-escalation and resulted in some practice changes preceded these dates.The most broadly disseminated form of value assessment in breast cancer surgery has come from the Choosing Wisely campaign  in women with average-risk unilateral breast cancer and SLNB in women aged >70 years with HR + , HER2-negative early-stage breast cancer20. Despite publication of these findings in 2013 and the subsequent recommendation in 2016 to avoid SLNB in patients aged >70 years with HR + breast cancer, the rate of use of this low-value procedure has remained relatively stable: 88% in 2013 and 87% in 20167. Qualitative evaluation of patients and providers revealed skepticism regarding supporting data, uncertainty about broad application of age-specific cutoffs, and perceptions that SLNB results in minimal morbidity as justifications for the continued use of SLNB. For example, patients and providers often dispute set age cutoffs in favor of assessment of an individual patient’s physiology. Finally, similar to the reasons underlying the continued use of CPM, the continued use of SLNB has been justified in terms of promoting patient peace of mind and allowing the determination of nodal involvement to confirm cancer staging21.A similar pattern has been observed for SLNB in older women with early-stage HR + cancer. Among patients in CALGB 9343, which randomized women aged >70 years with HR + clinical stage I breast cancer to tamoxifen plus radiation therapy or tamoxifen alone after lumpectomy, radiation therapy conferred a small benefit in the rate of locoregional recurrence but did not result in longer overall survival. Furthermore, there was no difference in survival was identified in patients who did and did not undergo axillary evaluation at the time of surgical excision. In long term follow up of the CALGB 9343 cohort, a 3% increase in axillary recurrence was noted in those who did not undergo radiation therapy or SLNB. Importantly, the result of SLNB in this cohort is unlikely to impact systemic therapy choice22. This has allowed for omission of ALND in node positive patients who downstage after neoadjuvant chemotherapy. Additionally, in node positive patients undergoing neoadjuvant chemotherapy, the clipping of positive nodes to allow for their removal at the time of surgery when they are not sentinel nodes has been demonstrated to further reduce the false negative rate in retrospective studies23. These approaches allow avoidance of ALND in 50–80% of node positive patients with triple negative and HER2 positive cancers24.In addition to the guideline alterations and shifts in practice described above, there are other emerging areas of surgical de-escalation that have impacted the surgical care of patients with breast cancer. For example, in node positive patients undergoing neoadjuvant chemotherapy, sentinel lymph node biopsy had been demonstrated to be a reliable method of assessment of axillary disease burden when dual tracer was utilized and three or more sentinel nodes are identified, with a false negative rate of <10%25. To uniformly and sustainably reduce the use of low-value practices, avoid overtreatment, and deliver the highest-quality evidence-driven care, deimplementation in breast cancer surgery will require strategies specifically targeted at the patient, physician, and society levels. These strategies may differ from those commonly used to reduce the use of low-value diagnostic testing or medications, such as ordering or formulary restrictions or market withdrawal.The differing outcomes among deimplementation efforts across four low-value breast surgical practices is a clear demonstration that effective de-escalation in breast cancer surgery relies on more than the availability of data from randomized controlled trials and other high-quality evidence. With substantial differences in the contexts of breast cancer care delivery and the strong emphasis on patient autonomy in decision-making, breast cancer surgery practices remain influenced by social, economic, and other factors that may affect deimplementation initiatives26. Additionally, even when patients report being aware of a lack of survival advantage in opting for more invasive surgery, patients still cite desire to extend their life as key to their decision making27.Deimplementation in breast cancer surgery should account for patients’ fears and, often, misperceptions of the risks of cancer recurrence, which can lead to patients being inclined to undergo more-invasive surgical interventions. For example, patients and their advocates may have concerns other than overall survival, such as fear of local recurrence or the desire to have peace of mind with their surgical decision. In these scenarios, patients may opt for treatments that do not necessarily convey a survival advantage but rather lead to reduced rates of local recurrence or contralateral breast cancer29. Patients considering more invasive surgical options may be best served by the use of patient education tools that emphasize communication of risks and introduce literature reporting on long term quality of life when comparing surgical interventions with comparable oncologic outcome. These efforts may help patients balance their desire for an overall excellent prognosis and low risk of recurrence with the potential costs and harms of overtreatment.Initial surgical treatment decisions may not fully consider the long-term harms or be able to anticipate potential decisional regret of choosing more invasive surgical treatment for breast cancer. For example, in consideration of contralateral prophylactic mastectomy in patients with nonhereditary breast cancer, patients often aim to reduce anxiety of recurrence through more invasive surgery, but may not actually benefit from reduced recurrence fear and may experience worse body image and quality of life following surgery21. Such clinicians may benefit from exposure to data, beyond the results of randomized controlled trials, focusing on the interaction of patient satisfaction and psychological outcomes after surgery. In addition to educational interventions, clinician-level strategies  may play a role in future deimplementation efforts in breast cancer surgery. On a broader scale, payment reform or a transition to value-based insurance may further influence decision-making in breast cancer surgery and promote the deimplementation of low-value practices. For example, payers may opt to incentivize facilities with high levels of appropriateness per current recommendations or implement value-based payment designs where low-value treatments may incur greater out of pocket costs to patients. Additionally, accreditation measures may have greater emphasis on appropriateness of care with collecting and reporting of data related to low-value practices.Clinicians often overestimate the benefits of procedures, underestimate the potential harms or costs of unnecessary intervention, and do not consider or misinterpret their patients’ values or desiresImportantly, de-escalation of breast cancer surgery must be conducted and monitored in the context of multidisciplinary care. As surgical practices are de-implemented, there may be a tendency toward escalation of adjuvant therapies. Specifically, in cases of uncertain nodal involvement when criteria for omission of SLNB was met, radiation may be more frequently offered or genomic assays more often used. These challenges in coordination between multidisciplinary care team members will be inherent in the changing landscape of breast cancer care and collaboration essential in order to resist unnecessary and potentially harmful care.32. However, the criteria for omission of surgery will likely spare only a minority of patients from undergoing excision, and the additional cost in terms of biopsies and surveillance and potentially increased anxiety may counteract any potential benefits of surgical de-escalation33. The presence of nonoperative trials signals that breast surgical oncologists will continue to be challenged to respond to the growing body of data, which may result in recommendations for even less surgery for patients with breast cancer.As the trend for less-intensive surgical interventions for breast cancer continues, the need for deimplementation will persist. Investigators are currently evaluating the safety of omitting surgery in the context of a complete clinical response to neoadjuvant therapy for invasive cancer, as well as the safety of omitting surgery from the management of patients with ductal carcinoma in situ34.Beyond interpreting the evidence from trials, the ability to successfully deimplement low-value breast cancer surgery will require an ability to adapt to new evidence, understand what is meaningful value for patients, and consider the interests and needs of all stakeholdersFurther information on research design is available in the Reporting Summary Checklist