bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning
Authors: Chang Chih-Wei , Marants Raanan , Gao Yuan , Goette Matthew , Scholey Jessica E , Bradley Jeffrey D , Liu Tian , Zhou Jun , Sudhyadhom Atchar , Yang Xiaofeng 
Published: September 2023

Objective
Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps.

Methods:
Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework.

Results:
For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: −0.72%/2.62%/−3.58% for adipose; −0.03%/−0.61%/−0.18% for muscle; −0.58%/−1.36%/−4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations.

Conclusion
The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps.

Advances in knowledge:
PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.
SpaceOAR hydrogel distribution and early complications in patients undergoing radiation therapy for prostate cancer
Authors: Yates Andrew H , Power Jack W , Dempsey Philip J , Agnew Adam , Murphy Brian D , Moore Richard , El Bassiouni Mazen , McNicholas Michelle M J 
Published: September 2023

Objectives:
Hydrogel spacers aim to separate the rectum from the prostate during radiation therapy for patients with prostate cancer to decrease the radiation dose and thus toxicity to the rectum. The aim of this study was to evaluate the distribution of the hydrogel spacer between the rectum and the prostate, to assess for hydrogel rectal wall infiltration and to assess for immediate complications

Methods:
Retrospective study of 160 patients who had undergone hydrogel spacer placement. Distribution of the hydrogel was assessed on MRI. MRI images were reviewed for rectal wall injection or other malplacement of gel. Early post-procedure complications were recorded.

Results:
117 (73.1%) patients had a symmetrical distribution of the hydrogel spacer. The mean anteroposterior rectoprostatic separation was 10.2 ± 3.7 mm (range 0–27 mm). Seven (4.3%) patient had minimal rectal wall infiltration and one (0.6%) patient had moderate infiltration. One (0.6%) patient had an intraprostatic injection of hydrogel. Two (1.3%) patients required treatment in the emergency department: one for urinary retention and one for pain.

Conclusions:
Transperineal hydrogel placement separates the prostate from the rectum with a symmetrical distribution in the majority of cases prior to radiation therapy with a low rate of rectal wall injection and immediate complications.

Advances in knowledge:
SpaceOAR hydrogel can be safely injected into radiation naive patients with low- or intermediate-risk organ-confined prostate cancer. The spacer separates the prostate from the rectum with a symmetrical distribution in the majority of cases prior to radiation therapy.
The kinetic quantitative characteristics of non-mass breast lesions with contrast-enhanced ultrasound: a prospective study
Authors: Cai Ying-Yu , Du Yi-Chao , Zhao Lei , Hu Wen-Jie , Bai Yun , Chen An , Du Lian-Fang , Li Fan 
Published: September 2023

Objective
To characterize non-mass breast lesions (NML) quantitatively by contrast-enhanced ultrasound (CEUS) and to evaluate its additional diagnostic value based on the Breast Imaging Reporting and Data System (BI-RADS) categories.

Methods
A prospective study was performed among consecutive patients with NMLs. All lesions were examined by grayscale ultrasound and CEUS and diagnosed on pathology. Standard mammograms were obtained in the patients over 30 years old. Three independent radiologists assessed the features on grayscale ultrasound and mammograms and classified NMLs according to BI-RADS categories. Combined with the quantitative analysis in CEUS, the BI-RADS categories were reassessed, and the sensitivity, specificity, positive-predictive value, negative-predictive value and area under the receiver operating characteristic curve (AUC) were calculated for the evaluation of the diagnostic performance.

Results:
30 benign and 24 malignant NMLs were finally enrolled in this study, with ductal carcinoma in situ being the majority of malignant (15/24). Average contrast signal intensity (AI), wash-in rate (WiR) and enhancement intensity at 40 s (I40) were found to be the most efficient kinetic parameters to diagnose malignant NMLs. Combined with the cut-off values of 205.2 for AI, 127.8 for WiR and 136.4 for I40, the diagnostic accuracy was improved (AUC = 0.904), with the sensitivity of 95.8% and the specificity of 70.0%.

Conclusion
The results suggested that hyperenhancement and rapid wash-in and wash-out are the characteristics of malignant NMLs. The kinetic analysis using CEUS can reflect hypervascular nature of malignant NMLs, thus improving the diagnostic performance combined with grayscale ultrasound.

Advances in knowledge:
In this study, we quantified the enhancement characteristics of non-mass breast lesions with CEUS. We revealed that the combination of CEUS and conventional ultrasound provided higher sensitivity for diagnosing malignant NMLs.
Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer
Authors: Lin Zijing , Gu Weiyong , Guo Qinhao , Xiao Meiling , Li Rong , Deng Lin , Li Ying , Cui Yanfen , Li Haiming , Qiang Jinwei 
Published: September 2023

Objectives: Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. Methods: The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. Results: Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828–0.942) and 0.810 (95%CI: 0.653–0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. Conclusions: The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. Advances in knowledge: This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.
Histogram-based analysis in progressive pulmonary fibrosis: relationships between pulmonary functional tests and HRCT indexes
Authors: Palmucci Stefano , Tiralongo Francesco , Galioto Federica , Toscano Stefano , Reali Linda , Scavone Carlotta , Fazio Giulia , Ferlito Agata , Sambataro Gianluca , Vancheri Ada , Sciacca Enrico , Vignigni Giovanna , Spadaro Carla , Mauro Letizia Antonella , Foti Pietro Valerio , Vancheri Carlo , Basile Antonio 
Published: September 2023

Objectives: To investigate relationships between histogram-based high-resolution CT (HRCT) indexes and pulmonary function tests (PFTs) in interstitial lung diseases. Methods: Forty-nine patients having baseline and 1-year HRCT examinations and PFTs were investigated. Histogram-based HRCT indexes were calculated; strength of associations with PFTs was investigated using Pearson correlation. Patients were divided into progressive and non-progressive groups. HRCT indexes were compared between the two groups using the U-test; within each group, baseline and follow-up Wilcoxon analysis was performed. Receiver operating characteristic analysis was used for predicting disease progression. Results: At baseline, moderate correlations were observed considering kurtosis and diffusion capacity of the lungs for carbon monoxide (DLCO) (r = 0.54) and skewness and DLCO (r = 0.559), whereas weak but significant correlations were observed between forced vital capacity and kurtosis (r = 0.368, p = 0.009) and forced vital capacity and skewness (r = 0.391, p = 0.005). Negative correlations were reported between HAA% and PFTs (from r = −0.418 up to r = −0.507). At follow-up correlations between quantitative indexes and PFTs were also moderate, except for high attenuation area (HAA)% −700 and DLCO (r = −0.397). In progressive subgroup, moderate and strong correlations were found between DLCO and HRCT indexes (r = 0.595 kurtosis, r = 0.672 skewness, r=-0. 598 HAA% −600 and r = −0.626 HAA% −700). At follow-up, we observed significant differences between the two groups for kurtosis (p = 0.029), HAA% −600 (p = 0.04) and HAA% −700 (p = 0.02). To predict progression, ROC analysis reported sensitivity of 90.9% and specificity of 51.9% using a threshold value of δ kurtosis <0.03. Conclusion: At one year, moderate correlations suggest that progression could be assessed through HRCT quantification. Advances in knowledge: This study promotes histogram-based HRCT indexes in the assessment of progressive pulmonary fibrosis.
MRI features to aid the identification of lateral temporal bone cephaloceles
Authors: Srinivasan Rohit , Obholzer Rupert J , Connor Steve EJ 
Published: September 2023

Objectives:
To evaluate ancillary MRI features which may aid the identification of lateral temporal bone cephaloceles (LTBCs).

Methods:
A retrospective cohort study analysed patients with MRI evidence of surgically confirmed spontaneous LTBCs as defined by intracranial contents traversing the tegmen tympani or mastoideum. Cases were identified from radiology and surgical databases. Two observers analysed three-dimensional T 2W temporal bone and whole brain imaging according to a priori criteria by consensus, with emphasis on the relationship of any adjacent cerebrospinal fluid (CSF) cleft to the defect. The contents, location, and clinical features of the LTBCs were recorded.

Results:
Eighteen patients (11 female, 7 male; mean age 59.3 years, age range 42–86 years) with 20 surgically confirmed spontaneous LTBCs (2 bilateral;16 unilateral) were evaluated. A temporal lobe sulcus or other CSF cleft extending to or traversing the defect was identified in 19/20 (95%) cases. Isointense CSF tympanomastoid signal was present in 41.2% cases, whilst superior semi-circular canal dehiscence was found in 40% of cephaloceles. At least two MRI features of idiopathic intracranial hypertension were seen in 38.9% patients. Cephaloceles were most commonly centred on the tegmen tympani (55%). Meningoencephaloceles were present in 95% cases.

Conclusion:
A temporal lobe sulcus or CSF cleft extending to or traversing the defect may aid the identification of LTBCs. Isointense CSF tympanomastoid signal, superior semi-circular canal dehiscence and MRI features of idiopathic intracranial hypertension are only present in under half of LTBCs.

Advances in knowledge:
The study details novel ancillary MRI features of LTBCs which may aid their identification.
Transarterial chemoembolization plus lenvatinib with or without a PD-1 inhibitor for advanced and metastatic intrahepatic cholangiocarcinoma: a retrospective real-world study
Authors: Ning Zhouyu , Xie Lin , Yan Xia , Hua Yongqiang , Shi Weidong , Lin Junhua , Xu Litao , Meng Zhiqiang 
Published: September 2023

Objectives:
Most patients with intrahepatic cholangiocarcinoma (ICC) present with locally advanced or metastatic disease. We report the combined potency of transarterial chemoembolization (TACE), lenvatinib and programmed cell death-1 (PD-1) inhibitors in patients with advanced and metastatic ICC.

Methods:
This retrospective study enrolled 32 patients with advanced or metastatic ICC between January 2017 and August 2021. Eligible patients had received gemcitabine-based TACE combined with lenvatinib with or without PD-1 inhibitor in any line of treatment. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method. Risk factors associated with OS were assessed using univariate and multivariate Cox regression analyses.

Results:
Eighteen patients received a combination of TACE and lenvatinib (TL group) and 14 patients received TACE and lenvatinib plus aPD-1 inhibitor (TLP group). The median follow-up time was 19.8 months (range 1.8–37.8). The median OS was 25.3 months (95% CI 18.5–32.1) and the median PFS was 7.3 months (95% CI 4.9–9.7). Partial response was achieved in 10 patients (31.3%), and stable disease in 13 (40.6 %) with disease control rate of 71.9%. The median OS was comparable in the TL and TLP groups (22.4 vs 27.3 months, respectively; hazard ratio: 1.245, 95% CI 0.4245–3.653; p = 0.687). The regression analysis revealed that, regardless of treatment group, a favorable independent prognostic factor for OS was HBV/HCV infection (HR: 0.063, 95% CI 0.009–0.463; p = 0.007). There were no treatment-related deaths and 81.3% of study participants experienced adverse events (AEs), the majority of which were of moderate severity (71.8% Grade 1–2).

Conclusions:
Gemcitabine-based TACE plus lenvatinib with or without aPD-1 inhibitor was well tolerated and provided promising therapeutic outcomes for patients with advanced and metastatic ICC.

Advances in knowledge:
Monotherapy with TACE, or Lenvatinib, or PD-1 inhibitors has shown limited efficacy over standard first-line chemotherapy in advanced and metastatic ICC. This work suggested the combined potency of these treatments and well-tolerance.
Pearls and pitfalls of structured staging and reporting of rectal cancer on MRI: an international multireader study
Authors: el Khababi Najim , Beets-Tan Regina GH , Curvo-Semedo Luís , Tissier Renaud , Nederend Joost , Lahaye Max J , Maas Monique , Beets Geerard L , Lambregts Doenja MJ 
Published: September 2023

Objectives:
To investigate uniformity and pitfalls in structured radiological staging of rectal cancer.

Methods:
Twenty-one radiologists (12 countries) staged 75 rectal cancers on MRI using a structured reporting template. Interobserver agreement (IOA) was calculated as the percentage agreement between readers (categorical variables) and Krippendorff’s α (continuous variables). Agreement with an expert consensus served as a surrogate standard of reference to estimate diagnostic accuracy. Polychoric correlation coefficients were used to assess correlations between diagnostic confidence and accuracy (=agreement with expert consensus).

Results:
Uniformity to diagnose high-risk (≥cT3 ab) versus low-risk (≤cT3 cd) cT-stage, cN0 versus cN+, lateral nodes and tumour deposits, MRF and sphincter involvement, and solid versus mucinous tumours was high with IOA &gt; 80% in the majority of cases (and &gt;80% agreement with expert consensus). Results for assessing extramural vascular invasion, cT-stage (cT1-2/cT3/cT4a/cT4b), cN-stage (cN0/N1/N2), relation to the peritoneal reflection, extent of sphincter involvement (internal/intersphincteric/external) and morphology (solid/annular/semi-annular) were considerably poorer. IOA was high (α = 0.72–0.84) for tumour height/length and extramural invasion depth, but low for tumour-MRF distance and number of (suspicious) nodes (α = 0.05–0.55). There was a significant positive correlation between diagnostic confidence and accuracy (=agreement with expert consensus) (p &lt; 0.001-p = 0.003).

Conclusions:
- Several staging items lacked sufficient reproducibility.
- Results for cT- and N-staging g improved when using a dichotomized stratification.
- Considering the significant correlation between diagnostic confidence and accuracy, a confidence level may be incorporated into structured reporting for specific items with low reproducibility.

Advances in knowledge:
Although structured reporting aims to achieve uniformity in reporting, several items lack sufficient reproducibility and might benefit from dichotomized assessment and incorporating confidence levels.
Modelling the influence of radiosensitivity on development of second primary cancer in out-of-field organs following proton therapy for paediatric cranial cancer
Authors: Dell'Oro Mikaela , Wilson Puthenparampil , Short Michala , Peukert Dylan , Bezak Eva 
Published: September 2023

Objective:
Radiobiological modelling the risks of second primary cancer (SPC) after proton therapy (PT) for childhood cranial cancer remains largely unknown. Organ-specific dose-response risk factors such as radiosensitivity require exploration. This study compared the influence of radiosensitivity data (slope of βEAR) on children’s lifetime attributable risks (LAR) of SPC development in out-of-field organs following cranial scattering and scanning PT.

Methods:
Out-of-field radiosensitivity parameter estimates for organs (α/β and βEAR) were sourced from literature. Physical distances for 13 out-of-field organs were measured and input into Schneider’s SPC model. Sensitivity analyses were performed as a function of radiosensitivity (α/β of 1–10 Gy) and initial slope (βEAR) from Japanese/UK data to estimate the influence on the risk of radiation-induced SPC following scattering and scanning PT.

Results:
Models showed similar LAR of SPC estimates for age and sex-matched paediatric phantoms, however, for breast there was a significant increase using Japanese βEAR data. For most organs, scattering PT demonstrated a larger risk of LAR for SPC which increased with α/β.

Conclusion:
Breast tissue exhibited the highest susceptibility in calculated LAR risk, demonstrating the importance for accurate data input when estimating LAR of SPC.

Advances in knowledge:
The findings of this study demonstrated younger female patients undergoing cranial proton therapy have a higher risk of developing second primary cancer of the breast tissue. Long-term multicenter registries are important to improve predictive radiobiological modelling studies of side effects.
Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography
Authors: Salim Mattie , Dembrower Karin , Eklund Martin , Smith Kevin , Strand Fredrik 
Published: September 2023

Objective: We aimed to evaluate the false interpretations between artificial intelligence (AI) and radiologists in screening mammography to get a better understanding of how the distribution of diagnostic mistakes might change when moving from entirely radiologist-driven to AI-integrated breast cancer screening. Methods and materials: This retrospective case–control study was based on a mammography screening cohort from 2008 to 2015. The final study population included screening examinations for 714 women diagnosed with breast cancer and 8029 randomly selected healthy controls. Oversampling of controls was applied to attain a similar cancer proportion as in the source screening cohort. We examined how false-positive (FP) and false-negative (FN) assessments by AI, the first reader (RAD 1) and the second reader (RAD 2), were associated with age, density, tumor histology and cancer invasiveness in a single- and double-reader setting. Results: For each reader, the FN assessments were distributed between low- and high-density females with 53 (42%) and 72 (58%) for AI; 59 (36%) and 104 (64%) for RAD 1 and 47 (36%) and 84 (64%) for RAD 2. The corresponding numbers for FP assessments were 1820 (47%) and 2016 (53%) for AI; 1568 (46%) and 1834 (54%) for RAD 1 and 1190 (43%) and 1610 (58%) for RAD 2. For ductal cancer, the FN assessments were 79 (77%) for AI CAD; with 120 (83%) for RAD 1 and with 96 (16%) for RAD 2. For the double-reading simulation, the FP assessments were distributed between younger and older females with 2828 (2.5%) and 1554 (1.4%) for RAD 1 + RAD 2; 3850 (3.4%) and 2940 (2.6%) for AI+RAD 1 and 3430 (3%) and 2772 (2.5%) for AI+RAD 2. Conclusion: The most pronounced decrease in FN assessments was noted for females over the age of 55 and for high density-women. In conclusion, AI could have an important complementary role when combined with radiologists to increase sensitivity for high-density and older females. Advances in knowledge: Our results highlight the potential impact of integrating AI in breast cancer screening, particularly to improve interpretation accuracy. The use of AI could enhance screening outcomes for high-density and older females.
Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration
Authors: Wei Lise , Niraula Dipesh , Gates Evan D H , Fu Jie , Luo Yi , Nyflot Matthew J , Bowen Stephen R , El Naqa Issam M , Cui Sunan 
Published: September 2023

Abstract
Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making.
In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.