bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel clinical trials imaging network platform for RECIST 1.1, and related protocols run on standard radiology software: A pilot at multiple oncology centers.
Authors: Goldstein Don , Hochster Howard S. , Malhotra Jyoti , Goldszal Alberto , Sokol Levi 
Published: June 2022

e13552 Background: Oncologists face the following challenges with clinical trials imaging analysis: non-structured radiology reports, inconsistent lesion measurement between time points, time-consuming and error-prone manual calculations for clinical trials protocols, and lack of dedicated storage and auditable signoff. Clinical Trials Imaging Network (CTIN) is a novel clinical trials imaging platform that allows radiologists to provide oncologists with dedicated clinical trials imaging reads and calculations from non-proprietary standard radiology dictation and PACS software, using RECIST 1.1 and other tumor response criteria. It was piloted at several cancer centers between 2019 and 2021. Methods: An anonymous survey was sent out to the oncologist and research coordinators who have experience using the CTIN program. The survey was designed to gauge the effectiveness of the software. Questions asked included the decrease in turnaround time between initiation of scan and completion of research protocol calculations, and regarding the decrease in time the oncologists personally need to make calculations. Additionally, respondents were inquired regarding the accuracy of the data, and how much time they had to spend verifying the integrity of the data. Results: The survey was sent to approximately 150 recipients, with 32 respondents. 84.4% of respondents said they strongly agreed or agreed that there was a decreased turnaround time between scan initiation, and completion of research protocol calculations, and 15.6% neither agreed nor disagreed. 80% strongly agreed or agreed that there was a decreased time they personally performed the calculations. 25.0% said they saved less than 5 minutes per time point personally performing the calculations, 28.6% said 5-15 minutes, 23.4% 15 to 30 minutes, and 17.8% 30-60 minutes. Users were also asked regarding their time spend verifying the data. 64.5% of people spent less than 5 minutes per time point verifying the integrity and quality of the data. 19.4% spent 5-15 minutes, 12.9% spent 15-30 minutes, and 3.2% 30-60 minutes. No one spent greater than 60 minutes verifying the data. 65.6% of people strongly agreed that they were confident in the accuracy of the data, 25.0% agreed, and 9.4% neither agreed or disagreed. 90.6% of people said that they were very satisfied or satisfied with CTIN. Conclusions: We have piloted an approach to provide clinical trials imaging metrics to oncologists using non-proprietary technology that runs from any standard radiology voice recognition system and PACS. The majority of respondents agreed that they would recommend this system for its time-saving, automated calculation and ease of use, user features. The CTIN platform bridges the gap between general radiology reads and clinical trials imaging reads without the need for dedicated radiology software.
ENLIGHT: Pancancer response prediction to targeted and immunotherapies via tumor transcriptomics.
Authors: Aharonov Ranit , Dinstag Gal , Shulman Eldad , Elis Efrat , Ben-Zvi Doreen , Tirosh Omer , Hoang Danh-Tai , SInha Sanju , Apolo Andrea B. , Dahut William L. , Lipkowitz Stan , Berger Raanan , Kurzrock Razelle , Papanicolau-Sengos Antonios , Karzai Fatima , Rajagopal Padma Sheila , Gilbert Mark R. , Aldape Kenneth D. , Beker Tuvik , Ruppin Eytan 
Published: June 2022

e13556 Background: Precision oncology is gradually advancing into mainstream clinical practice. Despite the significant recent growth in the number of approved biomarkers for immune and targeted therapies, demonstrating significant survival benefits, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We developed ENLIGHT - a transcriptomics-based computational platform that identifies and utilizes clinically relevant genetic interactions (GIs) to predict a patient’s response to cancer treatments. ENLIGHT markedly extends and improves upon SELECT, a previous GI-based framework for obtaining transcriptomics-based response biomarkers. In this study, we focus on three key translational aspects: (i) the number of drugs for which predictions can be obtained (ii) defining a biomarker-based test for Personalized Medicine (PM) that would identify favorable treatments for an individual; and (iii) Improving clinical trial design by excluding a sub-population of patients likely not to respond to the treatment. A key translational feature of the approach is that it does not require training on treatment response data. Thus, in addition to its ability to predict patients' response to approved and well-studied therapies, it can predict the response to new, unexplored treatments. Results: We first tested Enlight in the PM scenario, analyzing 21 patient cohorts from diverse indications, treated with a variety of targeted and immunotherapies. The ENLIGHT treatment matching score is associated with better response with an aggregate Odds Ratio (OR) of 2.59 (95% confidence interval [1.85, 3.55], p= 3.41 e-8). Applied to the WINTHER trial data, encompassing multiple indications and individualized treatments, ENLIGHT recommendations achieved a highly remarkable OR of 11.15 ([2.3, 54.5], p = 8 e-04), demonstrating ENLIGHT’s strong predictive power across a broad spectrum of treatments and cancer types. Second, using ENLIGHT to exclude patients from clinical trials increases the trial response rate, achieving more than 90% of the response rate attainable under an optimal exclusion strategy. Conclusions: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies in translationally oriented, clinical terms, going beyond case-specific biomarkers.
BRCANet: A deep hybrid network in predicting BRCA1/2 gene mutation of breast cancer with dynamic contrast-enhanced breast MRI.
Authors: Cao Wen-Ming , Wang Xiaojia , Liu Jian , Wang Lili , Zhang Xiaodong , Pan Jiani , Ye Weiwu , Chen Zhanhong , Zheng Yabing , Shao Xiying , Xu Yuanji 
Published: June 2022

e13576 Background: BRCA1/BRCA2, representing as an important genetic biomarker of breast cancer (BCA), can provide clinically significant implications for personalized risk assessment, effective treatment option, and prognostic prediction. Methods: We here proposed the BRCANet, a novel end-to-end convolutional neural network for noninvasively determining BRCA1/BRCA2 mutation by integrating clinical, radiomics and deep learning of dynamic contrast-enhanced (DCE) MRI. BRCANet accepts different forms of medical data including clinicopathologic identifications, high throughput radiomics and deep imaging features of breast MRI using a deep hybrid neural network for data/feature integration. Model training and cross-validation was performed in 132 case-controlled BCA patients from two in two tertiary care hospitals, in which clinicopathologic, genomic and image data of BCA lesions were available and center-standardized for study analysis. Results: Results show that a BRCANet-Plus model, embedded with clinicopathologic, radiomics and deep MRI features achieves an arear under curve of (0.783; 95% confidence intervals [CIs], 0.704 - 0.848) for predicting BRCA1/2 mutation, outperforming the compared state-of-the-art methods, i.e., BRCANet derived from image-only data (0.743; 95% CIs, 0.659 - 0.815; p = 0.037), and BRCARad derived from radiomics-only data (0.734; 95% CIs, 0.649-0.807; p = 0.031). After net benefit evaluation, the proposed BRCANet-Plus shows promise to improve diagnostic performance against conventional clinical or image approaches. Conclusions: Therefore, we concluded the presented deep hybrid approach by integrating multimodal clinical-imaging data, especially breast MRI, have a great potential to predict BRCA1/2 mutational status of BCA. This proof-of-concept strategy can be utilized for studying similar clinical questions.
Prediction of pathological grading of high-risk solitary pulmonary nodules based on CT imaging deep learning: A multi-centric study.
Authors: Lin Weihuan , Zhan Weijie , Zhou HaiYu 
Published: June 2022

e13577 Background: Histopathological classification of operable early-stage lung adenocarcinoma may influence the intraoperative surgical decision and postoperative management for patients. However, it is difficult to comprehensively evaluate the grade of risk of operable lung adenocarcinoma based on preoperative CT imaging alone. Therefore, we aimed to establish a pathological grading model for patients with pulmonary nodules through preoperative CT imaging. Methods: This was a diagnostic, retrospective, multi-centric study conducted in two independent centers from January 1, 2019, to June 1, 2021. After inclusion and exclusion criteria, a total of 396 patients were included from two centers and were diagnosed as stage IA lung invasive adenocarcinoma (IAC). Patients (n=308) from the Guangdong Provincial People's Hospital with solitary pulmonary nodules were randomly divided into the training cohort (n=222) and the internal validation cohort (n=86). Patients from the Jiangxi Cancer Hospital comprised the external cohort (n=88). Their clinical characteristics and preoperative CT images were collected at the same high-quality standard. Extracted radiomic features from tumor and peritumor areas were selected via the least absolute shrinkage and selection operator (LASSO) algorithm for a radiomics model (RM) construction. Logistic Regression analysis was conducted to develop a novel multi-parameter model (MPM) that integrated clinical characteristics and radiomic features. The area under the curve (AUC) of the receiver operator characteristic curve (ROC) was calculated to compare the prediction performance of models. Results: The MPM composed of clinical characteristics and radiomic features performed better than RM. It achieved good predictive value for high-risk IAC. The AUC of MPM in the training cohort was 0.87 (95%CI, 0.81-0.92), which achieved better identification performance than the RM (See Table). Similarly, model comparisons were conducted in the two validation cohorts in which the MPM gained high AUC values of 0.78 (95%CI, 0.68-0.88) and 0.83 (95%CI, 0.74-0.91), respectively. In addition, the sensitivity of MPM was higher than 80.0% in the three cohorts. Conclusions: The MPM may be a practical and reliable tool for predicting the pathological risk of patients with solitary pulmonary nodules. [Table: see text]
Application of deep learning models on whole slide images uncover new histological markers related to high-risk malignant pleural mesothelioma.
Authors: Hartman Douglas , Le Douget Jean-Eudes , Ye Ye , Li Yaming , Sin-Chan Patrick , Pronier Elodie , Becich Michael 
Published: June 2022

e13580 Background: Malignant Pleural mesothelioma (MPM) is a highly aggressive cancer of the pleural surface and represents 80-90% of mesothelioma diagnosis. MPM is broadly subclassified into three histological subgroups (epithelioid, sarcomatoid, biphasic), however tissue heterogeneity has resulted in diagnostic challenges and suboptimal patient care. There are currently no specific histological markers of high/low-risk MPM patients, which is critical in predicting patient prognosis. Methods: Owkin developed MesoNet, a deep learning model that predicts overall survival (OS) of MPM patients from whole slide images (WSI) and trained on the French MESOBANK and TCGA cohorts (Courtiol et al, 2019). In this study, we sought to validate MesoNet’s performance on an independent cohort of 127 WSI stained with haematoxylin/eosin from MPM patients collected at the University of Pittsburgh as part of the National Mesothelioma Virtual Bank (funding by U24OH009077). Patient demographics, survival data, expertly curated pathology annotations were also collected. Results: Our analyses showed that MesoNet predicted OS as risk score based on WSI, which validated high-risk MPM patients exhibited poorer OS, as compared to low-risk patients. Analyses on histological subtypes revealed sarcomatoid and biphasic patients were overrepresented in high-risk groups, as compared to epithelioid patients, which correlates with observed OS data. Notably, histological features associated with high-risk patients revealed tumor pleomorphism and anaplastic nuclear features, whereas low-risk tiles appear to be enriched in tumor infiltrating lymphocytes (TILs) with accompanying stromal proliferation and dense fibrosis. Conclusions: Collectively, our studies validate MesoNet performance on an independent cohort and identify new features related to MPM risk groups, which may inform future treatment stratification and personalization of immunotherapies.
Overcoming barriers in academic-industry partnerships to improve predictive modeling in immuno-oncology.
Authors: Mittendorf Kathleen F. , Micheel Christine , LeNoue-Newton Michele , Rahman Protiva , Fabbri Daniel , Wolber Jan , Osterman Travis John , Fouda Mohamed , Park Ben Ho 
Published: June 2022

e13581 Background: In the past decade, immunotherapies have revolutionized oncology practice by prolonging patient survival in previously rapidly fatal cancers. However, severe immune toxicities present a challenge, affecting ̃20% and up to 50% of patients on immune monotherapies and combination immunotherapies, respectively. Oncologists must balance toxicity risk with potential efficacy, and pharmaceutical companies have a vested interest in selecting patients with the highest benefit–risk ratio during trial enrollment. Predictive toxicity–efficacy modeling has the potential to guide trial subject selection and clinical care, yet there remains a need for predictive models that can be practically implemented in these settings. Methods: A common academic–industry contract data–transfer framework—wherein academic medical institutions and industry counterparts act in isolation—creates barriers to development of high-quality algorithms with practical applications. In this framework, 1) academic medical institutions provide patient data as a “data dump;” these data are static and cannot be refined—reducing opportunities for quality control; 2) predictive model outcomes may include artifacts that are not identified; 3) manual curation of patient data is required to accurately replicate the model in real-world settings; and 4) lack of clinician participation reduces the potential clinical applications of models and reciprocal benefit to the academic institution. We outline a more contemporary, engaged approach to unite strengths of both partners to achieve a common goal. Results: In 2019, Vanderbilt University Medical Center and GE Healthcare partnered with the goal of enabling safer and more precise immunotherapy use. As part of this work, we formulated a recipe for academic–industry partnerships that offers unique advantages over a static contract framework. In our iterative, interactive approach, 1) clinical and curation experts meet with industry modelers to dynamically refine deidentified data sets by resolving discrepancies in data from different sources (e.g., manually curated vs. structured data); 2) clinical experts iteratively review outputs of predictive models to identify potential artifacts and refine final models; 3) expert curators iterate with in-house machine-learning experts to create algorithms to automate curation of natural language elements from the identified EHR data; and 4) clinical and industry stakeholders participate in regular meetings with modelers to ensure clinical and trial utility of the modeling approach. Conclusions: Compared to data transfer-only relationships, this partnership framework offers an opportunity to develop more informed, higher quality immunotherapy models with clinical and industry applications.
Using tandem repeat genomic features for cancer signal detection across multiple cancer types.
Authors: Solovyev Victor , Tobias Sidney , Wong Edmund , Yip Shun H , Tang Cheuk Ying 
Published: June 2022

e13586 Background: Next generation sequencing methods enable the identification of molecular signatures predictive of cancers. Large-scale cancer genomic projects such as The Cancer Genome Atlas (TCGA) molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. The resulting data provides an opportunity to uncover a list of recurrent genomic aberrations, such as mutations, amplifications, insertions, and deletions, that can be used in machine learning models for multiple cancer type detection. Previous research has demonstrated significant variability in the stability of tandem repeat sequences between different cancer types, with consequences for gene expression, which is consistent with known oncogenic mechanisms. Methods: Our approach identifies a set of tandem repeats from whole-exome sequencing data to analyze, and computes differences relative to the reference genome in the cancer patients’ samples. For each tandem repeat sequence (referred to hereafter as TRS), we count the frequency of occurrence of the TRS in its reference state, as well as its specific variations (deletions/insertions) in each cancer patient exome sequence. These TRS read counts are the features for our cancer type prediction model. Through filtering out features of lower significance, we condense our training and testing datasets down from a half million possible features per sample, to merely thousands of features common across all the samples. From there, we train multi-class one-vs-all logistic regression models rapidly and optimize our feature selection and preprocessing to maximize predictive accuracy. Results: As can be seen in the Table below, our logistic regression model trained on TRS features enables us to predict cancer type with high accuracy in TCGA patient samples (as compared with random prediction accuracy, which would be approximately 10%). Conclusions: This approach lays the groundwork for novel TRS-based genetic tests for early detection and diagnosis of multiple types of cancer. [Table: see text] [Table: see text]
Oncology clinical trial termination trends: Analysis across the prior 20 years.
Authors: Palmeri Laura , Derkach Sergii , Vidal-Fisher Liat , Xue X.Q , Harb Wael A. , Moss Keren Rachel 
Published: June 2022

e13602 Background: Terminations of oncology clinical trials are an unavoidable potential outcome when novel anti-cancer therapeutics are evaluated, yet are a significant burden on cancer patients, institutions, regulators and the industry as a whole. Overall, approximately 1 in 5 trials are terminated – the four main reasons are poor enrollment (PE), business decisions, lack of efficacy, and toxicity. The objective of our study is to describe trends in trial terminations. Methods: We searched Citeline® Trialtrove database for oncology, phase 1-3, industry sponsored trials, started 1Jan2000-31Dec2019 listed as completed or terminated. Data collection was performed in Jan2022. For analysis purposes, they were grouped based on start year into four 5-year periods: I-2000-2004, II-2005-2009, III-2010-2014, IV-2015-2019. Undetermined phase, phase 4 and non-oncology trials were excluded. Parameters collected: phase, reason for termination (more than one could be listed), sponsor (academic involvement; Top20 pharma as determined by Scrip 100 annually). Results: A total of 21,125 trials met search criteria, of which 4,596 (22%) were indicated as terminated. Termination rate increased from period I to II (17% to 24%) and remained at a steady 22% in periods III, IV. When evaluating the rate of termination according to phase, over periods I-II-III-IV we observed an increase in the rate of termination of phase 1/2 trials - 10%, 17%, 19% and 20% respectively. Across other phases, the change in termination rates was similar to change in their proportional representation per period. In terms of reason for termination, PE remained a major reason overall, 1275 trials (28%) were terminated due to poor enrollment with a low rate of 13% in period I, followed by 37%, 29%, 21% in periods II, III, IV. Of the trials that were terminated due to PE, 51% had academic, and 73% had Top20 involvement. This is a higher representation that their respective involvement in trials. Conclusions: According to the results of our analysis, the proportion of oncology industry-sponsored terminated trials remained high over the last 20 years, with early phases (1, 1/2 & 2) being the most affected. PE accounted for almost 1/3 of the overall terminations, with a possible trend of decrease. In terms sponsor type, there appeared to be a higher number of trials terminated due to PE with Top20, and with academic involvement, compared to their representation in the overall numbers. It is yet to be determined whether innovation in oncology early phase drug development, such as biomarker driven trials and seamless phase transitions will impact termination rates. Further analysis is ongoing focusing on early phase oncology trials.[Table: see text]