bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: A multicentre study.
Authors: Hu Yalan , Cai Zhenhai , AiErken NiJiati , Liu Yueqi , Shao Nan , Shan Zhen , Zhang Xiaoling , Lin Ying 
Published: June 2023

e13542 Background: The study aimed to develop and optimize a radiomics model with features extracted from breast intra- and peri-tumoral region in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4, thereby obviating unnecessary biopsies. Methods: We retrospectively analyzed data of women with BI-RADS 4 or 5 lesions from three hospitals. The regions of interest (ROIs) of intra-tumoral regions were delineated manually on each slice of image. The peri-tumoral regions were then obtained by equidistant 3-dimensinal dilation of the tumor border with 1, 2 and 3mm, respectively. Seven classification models were built with features extracted from the intra- and peri-tumoral regions, including the Intra, Peri1mm, Peri2mm, Peri3mm, Intra+peri1mm, Intra+peri2mm and Intra+peri3mm. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC) and compared by DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern. To assess the rate of avoidable biopsies, three exploratory cutoff values on the ROC curve were examined. Results: The Comb2 model, built with features from peri-tumoral 2mm and intra-tumoral region, demonstrated the best performance with AUCs of 0·901 and 0·863 in the development and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement lesions. At the three exploratory cutoff values on the ROC curve, the model identified 11·2% (sensitivity of C1≥90%), 49·5% (sensitivity of C2≥95%) and 63·3% (sensitivity of C3≥98%) of the benign lesions in the development cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions. Conclusions: An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.
Clinicopathological characteristics, treatment patterns, and prognosis of male breast cancer: China versus the United States.
Authors: Gao Yuxuan , Zhang Mengmeng , Shao Nan , Shan Zhen , Zhang Yunjian , Jiang Zefei , Lin Ying 
Published: June 2023

e13543 Background: Male breast cancer (MBC) is a rare disease. There have been several relatively large-scale MBC retrospective studies based on the National Cancer Database (NCDB) and the Surveillance Epidemiology and End Results (SEER) database, both of which recorded the information about American patients. However, relevant studies in China are still very scarce. We aimed to explore the characteristics and prognostic information of Chinese MBC patients from a more comprehensive perspective by comparing them with US patients. Methods: We identified 912 male patients who received breast cancer diagnosis between 1988 to 2022 from 31 centers in total and extracted their clinicopathological, therapeutical and prognostic information from Chinese Society of Clinical Oncology database and other cooperative centers in China. The corresponding information about US patients from the same period were also extracted from the SEER database. Our analysis finally included 883 Chinese and 3751 US patients. Kaplan-Meier curves, log-rank test and the Cox proportional hazards regression models were used to analyze and compare survival and identify prognostic factors. Results: Comparing with US MBC patients, Chinese patients showed a younger age (median age group 55 - 64 years in China vs 65 - 74 years in US, P<0.001) but more advanced AJCC stages (18.2% vs 14.5% in stage III and 7.7% vs 7.4% in stage IV, P<0.001). The majority of patients from both countries were ductal carcinoma (infiltrative and in situ) and luminal type. Chinese MBC patients were more likely to receive mastectomy (88.2% vs 58.4%, P<0.001) and chemotherapy (49.5% vs 33.0%, P<0.001). About 63.3% of Chinese patients received endocrine therapy, which was not recorded in SEER database. Chinese patients also showed a better prognosis both in 3-year (91.9%, 95% CI 89.7% - 94.1% vs 79.2%, 95% CI 77.9% - 80.6%) and 5-year (85.0%, 95% CI 81.9% - 88.2% vs 67.3%, 95% CI 65.7% - 68.9%) overall survival rates. Multivariate Cox model confirmed that age (P<0.001, HR=1.034, 95% CI 1.018 - 1.051), AJCC stages (comparing with stage I, P=0.894 for stage II; P=0.038, HR=1.963, 95% CI 1.037 – 3.717 for stage III and P<0.001, HR=4.001, 95% CI 2.035 – 7.868 for stage IV), mastectomy (P=0.002, HR=0.357, 95% CI 0.184 - 0.692) and endocrine therapy (P<0.001, HR=0.373, 95% CI 0.245 - 0.570) were independent prognostic factors of overall survival for Chinese MBC patients. Conclusions: With the largest scale for MBC patients so far in China, our findings revealed the exclusive features of Chinese MBC patients, who were diagnosed at a younger age, more advanced in AJCC stages, more conservative in surgery selection, more chemotherapy use and higher in 3-year and 5-year overall survival rates than their US counterpart. We also identified that age, AJCC stages, mastectomy and endocrine therapy were independent prognostic factors for overall survival of MBC patients in China.
Toward automating RECIST 1.1: Improving AI new lesion detection with longitudinal image data.
Authors: Gong Amanda , Daly Morgan , Melendez-Corres Gabriel , Teng Pang Yu , Hukkeri Tanmay Sanjay , Muhammad Wahi-Anwar , Ruchalski Kathleen , Goldin Jonathan , Brown Matthew S. 
Published: June 2023

e13545 Background: Automating RECIST 1.1 would offer benefits in speed, consistency, and cost of clinical trial imaging reads. Detection of new lesions is subjective and a common source of reader adjudication. Current AI lesion detection typically uses single timepoint images. The use of temporal data in AI is a rapidly evolving field in non-medical applications and new lesion detection in response assessment may benefit from longitudinal multi timepoint images. We explore the impact of longitudinal image input to deep neural networks for new lesion detection; as a proof of concept, we compare single vs dual timepoint input in new liver lesion detection in CT. Methods: We utilized CT images from two public datasets: 1) diseased liver (DL), a subset of DeepLesion (doi: 10.1117/1.JMI.5.3.036501), and 2) healthy liver (HL) from potential liver donors (Med Image Anal 69 (2021) 101950).. Due to the lack of a publicly available longitudinal image dataset with new lesions, we simulated (“sim”) a longitudinal study by registering HL scans (sim healthy baseline) with DL scans (sim follow up with new lesion) and selecting well-paired images using mutual information. From each pair of scans, we select one positive (with new lesion on follow up) and one negative (without lesion on follow up) 6x6cm image patch from within the liver, creating a dataset of 3296 patches with a balanced number of pos/neg samples. We trained two deep neural networks to classify the presence of a new liver lesion: 1) a conventional single timepoint model (ST) trained on DL patches only, i.e. using the follow up scan only, and 2) an experimental dual-timepoint model (DT) trained on patch pairs from DL-HL paired scans. We used 5-fold cross validation and paired t tests to compare model AUC and accuracy. Results: Of 10,594 CT studies (4,427 patients, pts) in DeepLesion, we paired 1648 DL scans (946 pts with liver lesions) with one of 20 registered HL scans (20 pts). The DT model outperformed the ST model, increasing AUC from 0.914 ± 0.002 to 0.929 ± 0.005 (p=0.040), with similar trends in accuracy (dataset split, Table 1). Conclusions: The DT model outperformed the ST model in the detection of new liver lesions, suggesting preliminary the benefit of multi-timepoint input to a deep neural network using sim longitudinal data. The use of longitudinal image data to improve interval change detection is a step towards automating radiologic response assessment in clinical trials. We plan to extend this work to automate new lesion detection in real clinical trial longitudinal images. Results (bold) are reported on test set across 5 folds. N image patches (ST) or patch pairs (DT). [Table: see text]
Validating the use of machine-learning cancer staging algorithms for Medicare cost analyses.
Authors: Smith Rebecca , Miller-Wilson Lesley-Ann , Cuyun Carter Gebra , Fayyaz Ifrah , Pope Andreyah , Pyenson Bruce 
Published: June 2023

e13548 Background: Administrative claims provide valuable real-world insight into the care of cancer patients; however, claims data lacks cancer stage information. This limitation constrains research on the value of early diagnosis and treatment as well as on the costs and savings associated with increased cancer screening. In prior work, our team used the SEER-Medicare data to develop machine learning (ML) algorithms to stage non-small cell lung (NSCLC), colon (CC), and rectal (RC) cancer patients using clinical flags derived from claims data. These algorithms were 69% (RC), 78% (NSCLC), and 83% (CC) accurate at matching incident cancer patients with their SEER-recorded AJCC stage (SEER-stage) at diagnosis. This work sought to test whether these ML algorithms are sufficiently accurate for use in claim cost analyses. Methods: Incident NSCLC, CC, and RC patients were identified using 2016-2017 SEER-Medicare data and assigned a cancer stage using a claims-based predictive multinomial logistic regression model (R Statistical Software - v4.1.2 R Core Team 2021; nnet package - Venables and Ripley 2002). Patients’ cumulative medical and pharmacy costs were summarized for 12 months starting with patients’ index month. Patients’ Medicare index month was set equal to the month of their first claim with a cancer diagnosis. Patients’ SEER index month was set equal to the diagnosis month associated with their incident tumor record in SEER. Patients with each cancer type were then grouped two ways - by ML-stage and by SEER-stage. Median patient costs were compared between stage groups for each cancer type and differences tested for statistical significance using Wilcoxon Rank-Sum Testing. Results: For NSCLC and CC, raw differences in median 12-month claim costs between the ML- and SEER-stage cohorts were small (1%-4%). Cost differences for RC were higher (7%-17%). ML and SEER costs were not significantly different (p > 0.05) between later-stage cohorts (NSCLC stages 3 and 4, CC stages 2C/3 and 4, and RC stage 4); however, early-stage groups were always significantly different (p < 0.05). Conclusions: Although costs were not statistically equivalent across all stage groups, the similarity of ML and SEER costs across higher-stage cohorts and small raw differences in median costs for each NSCLC and CC group suggests that ML algorithms with higher accuracy may be used to develop costs from administrative data for stage shift modeling and cost tradeoff analyses. [Table: see text]
Post-pandemic outlook of the oncology clinical trial landscape.
Authors: Pant Jyoti , Jadhav Aparna , Seth Roshan , Raghupathy Gayathri 
Published: June 2023

e13549 Background: The oncology clinical trials have grown gradually until 2019. The COVID-19 pandemic led to a marked reduction in clinical trial numbers. To understand the post-pandemic oncology landscape, we analyzed recent clinical trials between 2021 and 2022. Methods: The analytics and insights were obtained from the Kognitic digital platform for 2021 and 2022 (extracted-January 2023). The Kognitic platform applies proprietary Natural Language Processing (NLP) to extract cancer clinical trials from NIH’s clinicaltrials.gov (https://clinicaltrials.gov/ct2/home) and World Health Organization international trials registry ( https://trialsearch.who.int/utn.aspx ). A list of cancer-related synonyms and keywords was used with the NLP algorithm to extract the trials and generate a normalized list which was used for quality check to classify and add to the Kognitic drug database. Results: 14,361 national and international clinical trials launched in 2021 and 2022 were collected and analyzed. Of 14,361 trials, 3906 did not have trial phase information and were excluded from further analysis. We observed a reduction in overall clinical trial numbers in 2022 compared to 2021, with 8000 trials in 2021 and 6361 trials in 2022. In 2021, there were 799 Early Phase trials, 1043 Phase I, 2665 Phase II, 971 Phase III, and 425 Phase IV trials. In 2022, there were 340 Early Phase, 1037 Phase I, 2375 Phase II, 617 Phase III, and 183 Phase IV trials. Of these five Phases, Phase II had the highest number of trials in both 2021 and 2022. Similar to the total number of trials, we observed a general trend of decreasing trial count in all Phases (except Phase I) in 2022 compared to 2021. These trials were distributed across 21 indications, with the highest trial count in gastrointestinal cancer for both years. Out of the total trials, 24.18% of trials in 2021 and 22.93% in 2022 were gastrointestinal cancer trials. Other leading indications were lung cancer, non-Hodgkin lymphoma, breast cancer, and gynecologic cancers. Although the top indications remain the same in 2021 and 2022, there is a general trend of a decreasing number of trials in these indications in 2022. Next, we looked at the targets these trials were focused on. Our results show that Programmed Cell Death Protein 1 (PD-1) was the top target with the highest number of clinical trials, suggesting a continued broadening of the checkpoint inhibitor market. Similar to the total number of trials and the indications, we observed a general trend of decreasing clinical trials in each target in 2022 compared to 2021. Conclusions: Our results show a general trend of decreasing clinical trials in 2022 compared to 2021, suggesting that oncology clinical trials have not resumed pre-pandemic period growth. We observed a reduction in the number of clinical trials collectively and within various categories, namely, trial Phase, indications, and targets.
Using EHR data and machine learning approach to facilitate the identification of patients with lung cancer from a pan-cancer cohort.
Authors: Yu Yue , Ruddy Kathryn Jean , Leventakos Konstantinos , Liu Bolun , Huo Nan , Pachman Deirdre R. , Zong Nansu , Xiao Guohui , Chute Christopher , Pfaff Emily , Cheville Andrea L. , Jiang Guoqian 
Published: June 2023

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]
Performance validation of an artificial intelligence-powered PD-L1 combined positive score analyzer in six cancer types.
Authors: Lee Taebum , Cho Soo Ick , Choi Sangjoon , Kim Sukjun , Jung Wonkyung , Lee Dasom , Lee Seungje , Mostafavi Mohammad , Park Seonwook , Lee Jinhee , Shin Jaewoong , Kim Seokhwi , Paeng Kyunghyun , Ock Chan-Young 
Published: June 2023

e13553 Background: Programmed death-ligand 1 (PD-L1) expression is a predictive marker for immune checkpoint inhibitors (ICI) treatment in various cancer types. The evaluation of PD-L1 expression level by combined positive score (CPS) correlates with immunotherapeutic response in biliary tract, colorectum, liver, pancreas, prostate, and gastric cancers. This study aimed to assess the performance of an artificial intelligence (AI)-powered PD-L1 CPS analyzer on these six cancer types, and to investigate whether the AI assistance could improve concordance among pathologists. Methods: Lunit SCOPE PD-L1 CPS was developed with 1.51 x 106 tumor cells and 8.73 x 105 immune cells from 2,372 PD-L1 stained whole-slide images (WSI) or tissue microarray cores from various cancer and normal tissues. The algorithm consisted of tissue area segmentation and cell detection AI models. The AI models calculated the CPS by detecting tumor cells over the tumor area and immune cells over the tumor and adjacent area. The model performance was validated on 135 PD-L1 stained WSIs including the six cancer types, which were interpreted by three pathologists. The concordant CPS classification (≥1 or <1) by two or more pathologists was considered as the consensus. Each pathologist revisited to evaluate WSIs with AI assistance (including visualization and scoring) if there was a discrepancy between the pathologist and the AI model. Results: Of 135 WSIs, 122 (90.4%) were classified as the same CPS subgroup by all three pathologists. The CPS ≥1 and <1 subgroup included 67 (49.6%) and 68 (50.4%) cases, respectively. The overall percent agreement (OPA) of the AI model to the consensus of pathologists was 84.4%, ranging from 78.3% (liver) to 91.3% (biliary tract). The AI-assisted re-evaluation by three pathologists was performed in 17, 19, and 22 WSIs, respectively. According to the AI-assisted revision, the unanimous agreement level was increased to 92.6% (125 cases). The OPA of the AI model to the consensus of pathologists was also increased to 91.9%, ranging from 82.6% (liver) to 100.0% (pancreas). Conclusions: This study shows that an AI-powered PD-L1 CPS analyzer can evaluate the CPS in the six cancer types analyzed here at a comparable level to pathologists. AI assistance can improve the concordance of pathologists' CPS interpretation.[Table: see text]
Impact of breast density on classification of infrared spectroscopy for breath-based breast cancer screening.
Authors: Naz Farah , Groom Amy Grace , Mohiuddin MD , Sengupta Arpita , Burnell Margot J. , Daigle-Maloney Trisha , Michael James Charles Roger , Graham Stephen , Beydaghyan Gisia , Morrell Christian Bailey , Larracy Robyn , Scheme Erik 
Published: June 2023

e13558 Background: While mammography is currently the standard of care for breast cancer screening, dense breast tissue can significantly degrade results. Alternatively, infrared spectroscopy analysis of breath offers a highly sensitive method for identifying exhaled volatile organic compounds (VOCs), which may circumvent issues with breast density. Methods: Alveolar breath samples were collected onto Tenax TA sorbent tubes using a SohnoXB™ breath sampler. Using four desorb temperatures (75, 150, 225 and 300°C), absorption spectra were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a technique for measuring absorption coefficients due to VOCs in exhaled breath. Missing values in the absorption spectra were backfilled using interpolation, and the spectrum was min-max normalized and quadratic detrended. First and second derivatives of the preprocessed absorption spectra were used as features for a support vector machine machine-learning model. Features were ranked based on minimum redundancy maximum relevance (mRMR). The top 20 ranked features were selected to limit the potential for overfitting and then optimized. Model performance was validated using non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV to provide optimistic and pessimistic results, respectively. Results: Absorption spectra from 111 participants (71 positive, 40 control) were used. Of the positive subjects, 30 had low- and 31 had high-density breast tissue (measures missing for 10). Model performance is outlined. A subgroup analysis compared model performance for subjects with low- and high-density breast tissue for both non-nested and nested LOOCV models. Breast density data was not captured for control subjects thus they were not included in the subgroup analysis. Fisher’s exact test was performed to assess for significant difference between model performance for those with low- vs high-density breast tissue, resulting in a p-value of 1.00. Conclusions: Our results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer that is independent of breast density. Breath analysis may therefore become a new alternative or corroborative to mammography to support clinical decision-making. [Table: see text]