bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trends in ALK inhibitors for non-small cell lung cancer.
Authors: Meade Denise , Ng Marie , Alford S 
Published: May 2020

e14046 Background: Rearrangements in the gene encoding anaplastic lymphocyte kinase (ALK) are found in 3%–5% of patients. Treatment options have significantly expanded with ALK inhibitor drug approvals including crizotinib in 2011, ceritinib in 2014, alectinib in 2015, brigatinib in 2017 and lorlatinib in 2018. We sought to better understand the real-world treatment utilization and cost of ALK inhibitors in lung cancer (LCA) over the most recent period for which adjudicated claims are available, October 2017-September 2018. Methods: A LCA disease model was built in IBM Access and Value Connect to interrogate patient demographics, payer types, clinical characteristics, clinical outcomes, utilization, costs, and treatment sequencing, informed by United States Food and Drug Administration (FDA) labels for approvals, professional society guidelines, clinical and economic literature review, and subject matter expertise. Patients were selected from IBM Marketscan Commercial and Medicare Claims database and have an approved LCA treatment, at least one diagnosis claim for LCA within 30 days of treatment, and a minimum treatment period of 90 days, but do not have claims for other cancers or pregnancy during the study period. Using advanced visual analytics, we identified trends in the cost, utilization, and clinical outcomes of LCA patients. Results: There were 2,300 LCA treated patients in our 12-month cohort from October 2017-September 2018, of which 6% were treated with an ALK inhibitor. Of those 145 patients, 59% were treated with alectinib and 33% with crizotinib. Overall all cause per-member per-month costs for alectinib and crizotinib patients were $18,541 and $19,078, respectively. While the overall costs for alectinib and crizotinib were roughly the same, ER and in-patient costs were higher for crizotinib at $1,635 and $404, respectively, compared to $503 and $57 for alectinib. Conclusions: Our analyses demonstrate alectinib is the preferred first-line ALK inhibitor in the last twelve months of available data. Furthermore, the increased ER and in-patient costs may substantiate the findings of the ALEX trial, notably higher liver toxicity and more nausea, vomiting, and diarrhea for crizotinib. There is not yet sufficient data on the newer ALK inhibitors, brigatinib and lorlatinib in the real-world.
Deep learning to develop transcriptomic model for survival prediction in TCGA patients with hepatocellular carcinoma.
Authors: Yu Hao , Dai Wei , Chiang Chi Leung , Du Shisuo , Zeng Zhao-Chong , Shi Guo-Ming , Zhang Wei , Chan Albert , Hu Chen , Kong Feng-Ming 
Published: May 2020

e14057 Background: This study aimed to investigate the prognostic value of transcriptome and clinical data of Hepatocellular carcinoma (HCC) patients for overall survival (OS) by deep learning method. Methods: A total of 371 HCC patients with 20530 level three RNA-sequencing data were from The Cancer Genome Atlas (TCGA). Cox-nnet model, a deep learning model through an artificial neural network extension of the Cox regression model, was used for OS prediction. The patients were randomly split into train-set and test-set (7:3). In train-set, the significant genes associated with OS under univariate Cox regression were considered for modeling. Clinical parameters, including age, gender, pathologic stage, child pugh classification, creatinine level etc. were also considered. The Cox-nnet model was developed by cross-validation. Its discrimination was determined by the concordance index (CI) in the independent test-set and compared with multivariable Cox regression. The clustering method Uniform Manifold Approximation and Projection (UMAP) was used for revealing biological information from the hidden layer in the model. Results: In the train-set (n = 259), 1505 genes and two clinical variables (child pugh score and creatinine level) were significantly associated with OS (adjusted P-value < 0.05). To avoid overfitting, only 40 most significant genes were included in the Cox-nnet model. In the test-set (n = 112), the CI of Cox-nnet (0.76, se = 0.04) is better than the CI of multivariable Cox regression (0.71, se = 0.05). The difference between good or poor survival subgroups classified by Cox-nnet was remarkably significant ( P-value = 1e-4, median OS: 80.7 vs. 25.1 months). In the Cox-nnet model with all significant variables, the weights in the hidden layer were clustered by UMAP into 3 positive clusters and 2 negative clusters, which are enriched in GO/KEGG. The “cell cycle” and “complement and coagulation cascades” are the most important signal pathways in positive and negative clusters, respectively. Conclusions: Combining transcriptomic and clinical data, and with deep learning algorithm, we built and validated a robust model for survival prediction in HCC patients. Our study would be useful to explore the clinical implications in survival prediction and corresponding genetic mechanisms. Clinical trial information: 5U24CA143799, 5U24CA143835, 5U24CA143840, 5U24CA143843, 5U24CA143845, 5U24CA143848, 5U24CA1438.
Emerging oncology drug targets in the 21st century: An FDA analysis.
Authors: Scott Emma C. , Gong Yutao , Subedee Ashim , Kazandjian Dickran , Pazdur Richard , Rao V. Ashutosh , Beaver Julia A. , Schneider Julie A. 
Published: May 2020

e14067 Background: Traditional chemotherapy agents are cytotoxic and typically act on all rapidly dividing cells. In contrast, targeted cancer therapies take advantage of unique genetic or proteomic susceptibility in cancer cells, their microenvironment, or the immune system to selectively target tumor tissue or stimulate immune activity. We catalogued oncology drug approvals in the U.S. in the 21st century to study the evolution of molecular targets over the last twenty years. Methods: We used internal FDA databases and data repositories to generate a dataset of all oncology approvals granted 1/1/2000-12/31/2019. This dataset was curated through a comprehensive survey of drug labels, drug reviews, and published literature to include target gene(s), mechanism of action, drug class, and approved indications for each drug. Approvals were grouped into five-year periods for trend analyses. Results: There has been an increase in the number of oncology approvals over the last twenty years, the vast majority of which are targeted therapies. There have been considerable recent advances in some disease sites. Three disease sites (ovary, bladder, and liver) had many approvals in the last five years after few to none 2000-2014; lung cancer and melanoma have seen substantial advances in the last ten years. Additionally, the first site-agnostic approvals occurred in the last five years. Kinase inhibitors are the most common drug class by both number of drugs and indications. Immune checkpoint inhibitors (ICI) are the second most common class by number of indications, despite having only entered the market in 2011 and being the sixth most common class by number of drugs. The target gene PD-1 has the highest number of approved indications, followed by EGFR and the BCR-ABL fusion gene. The number of novel genes targeted by drugs approved in 2010-2019 is nearly double that of 2000-2009, with the most novel gene targets in 2019 (n = 7). Conclusions: This analysis captures the landscape of targeted drug approvals in the 21st century, including the continued dominance of kinase inhibitors despite the dramatic impact of ICIs on cancer care since their introduction in 2011. The promise of targeted therapy in oncology is especially evident from the introduction of site-agnostic indications, new approvals in cancer types with limited therapy options, and ICIs with many indications due to targeting tumors indirectly via the immune system. The continual introduction of novel gene targets provides a snapshot of ongoing novel drug development, and it will be interesting to see whether this trend continues over the next twenty years.
Evaluation of an AI system for breast cancer screening in mammograms of young women.
Authors: Petrini Daniel Gustavo Pellacani , Valente Gabriel Vansuita , Shimizu Carlos , Roela Rosimeire Aparecida , de Araújo Gabriel Miranda , Tucunduva Tatiana Cardoso de Mello , Koike Folgueira Maria A. A. , Kim Hae Yong 
Published: May 2020

e14068 Background: The interpretation of the mammography is challenging, especially in young women, who have dense breasts. Artificial intelligence (AI) promises to improve breast cancer detection; however these systems should be tested on different datasets. Our aim is to evaluate the performance of a publicly available deep convolutional neural network, developed by Wu et al. (IEEE Trans. Med. Imaging, 2019), applied to mammograms of young women. Methods: The test dataset consisted of mammograms obtained on a single occasion from 135 young women (up to 40 years old) on a Siemens mammography system. Each exam consisted of 4 full-field digital mammography images and had two labels (left malignant and right malignant). Mammograms were analyzed by a single mammography trained radiologist, using BI-RADS reporting tool. Among 270 labels, 170 were malignant and 100 were non-malignant. We used the program developed by Wu et al. that, according to the authors, presents AUC of 0.895 for the general population. As a preliminary test, we ran this program in a publicly available dataset named INbreast and obtained AUC of 0.8708, very close to the result reported by the authors. Results: We applied the program to our dataset of young women and obtained AUC of 0.876. We computed its standard error, obtaining 0.0290. At equal error rate point of the ROC curve, specificity and sensitivity are both 0.774. With this result we conclude that, at least for our dataset, cancer detection in young women is not substantially more difficult than in general population for an AI system. We fine-tuned the weights of the original network to the population of young women using transfer learning and obtained a slight improvement in AUC: 0.9018±0.0528, where the mean and the standard error were obtained using 5-fold cross validation. As the improvement was small and the standard errors are large, we would have to test on a larger test set to ensure that the observed improvement is real. Conclusions: We conclude, based on the experimental data, that there is no substantial degradation in accuracy when a mammogram screening program for general population is used for young women. We also conclude that it seems to be possible to obtain a slight improvement in accuracy by fine-tuning the network for the population of young women.
Machine learning analysis on American Gut Project microbiome data to identify subjects with cancer both with and without chemotherapy exposure.
Authors: Akbilgic Oguz , Karabayir Ibrahim , Gunturkun Hakan , Pierre Joseph F , Rashe Ashley C , Thomas Alexandra 
Published: May 2020

e14069 Background: There is growing interest in the links between cancer and the gut microbiome. However, the effect of chemotherapy upon the gut microbiome remains unknown. We studied whether machine learning can: 1) accurately classify subjects with cancer vs healthy controls and 2) whether this classification model is affected by chemotherapy exposure status. Methods: We used the American Gut Project data to build a extreme gradient boosting (XGBoost) model to distinguish between subjects with cancer vs healthy controls using data on simple demographics and published microbiome. We then further explore the selected features for cancer subjects based on chemotherapy exposure. Results: The cohort included 7,685 subjects consisting of 561 subjects with cancer, 52.5% female, 87.3% White, and average age of 44.7 (SD 17.7). The binary outcome variable represents cancer status. Among 561 subjects with cancer, 94 of them were treated with chemotherapy agents before sampling of microbiomes. As predictors, there were four demographic variables (sex, race, age, BMI) and 1,812 operational taxonomic units (OTUs) each found in at least 2 subjects via RNA sequencing. We randomly split data into 80% training and 20% hidden test. We then built an XGBoost model with 5-fold cross-validation using only training data yielding an AUC (with 95% CI) of 0.79 (0.77, 0.80) and obtained the almost the same AUC on the hidden test data. Based on feature importance analysis, we identified 12 most important features (Age, BMI and 12 OTUs; 4C0d-2, Brachyspirae, Methanosphaera, Geodermatophilaceae, Bifidobacteriaceae, Slackia, Staphylococcus, Acidaminoccus, Devosia, Proteus) and rebuilt a model using only these features and obtained AUC of 0.80 (0.77, 0.83) on the hidden test data. The average predicted probabilities for controls, cancer patients who were exposed to chemotherapy, and cancer patients who were not were 0.071 (0.070,0.073), 0.125 (0.110, 0.140), 0.156 (0.148, 0.164), respectively. There was no statistically significant difference on levels of these 12 OTUs between cancer subjects treated with and without chemotherapy. Conclusions: Machine learning achieved a moderately high accuracy identifying patients’ cancer status based on microbiome. Despite the literature on microbiome and chemotherapy interaction, the levels of 12 OTUs used in our model were not significantly different for cancer patients with or without chemotherapy exposure. Testing this model on other large population databases is needed for broader validation.
Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.
Authors: Kanan Christopher , Sue Jillian , Grady Leo , Fuchs Thomas J. , Chandarlapaty Sarat , Reis-Filho Jorge S. , Salles Paulo G O , da Silva Leonard Medeiros , Ferreira Carlos Gil , Pereira Emilio Marcelo 
Published: May 2020

e14076 Background: The most common approach to diagnose prostate cancer is the “whole gland biopsy procedure,” in which numerous cores (≥12) are taken from different regions of the gland to maximize the chances of detecting small cancers; the presence of cancer in any of these cores is significant to the patient. If concerning features that are not fully diagnostic of cancer are identified, the pathologist may defer the final diagnosis until additional studies (e.g. immunohistochemistry) have been performed. We recently developed an artificial intelligence (AI)-based system for the assessment of cancer in prostate biopsies. Here, we investigated the performance of this test in an independent dataset of prostate cancers consecutively accrued. Methods: Two board-certified pathologists retrospectively reviewed 600 digitized hematoxylin-and-eosin (H&E) stained diagnostic prostate core needle biopsy slides from 100 consecutive patients, originally diagnosed at an independent hospital. Pathologists’ assessments were based on the H&E image alone; if further testing would be preferred, it was noted in the review notes. All images were assessed by Paige Prostate 1.0, an AI-based diagnostic tool; based on its outputs (either suspicious for cancer or not), the discordant images were re-reviewed by the pathologists and, in parallel, adjudicated with additional testing (e.g. ancillary immunohistochemical markers). Results: Paige Prostate's slide-level sensitivity was 98.9% and its specificity was 93.3% (100% and 78.0%, respectively, at the subject-level). The pathologists' average slide-level sensitivity and specificity without Paige Prostate was 90.9% and 98.6%, respectively. The sensitivity with their consensus read and Paige Prostate increased by 5.7% to 96.6% with only 0.8% decrease in specificity. In addition to new slide-level findings, benefits were also observed at the subject-level; with Paige, three new prostate cancer cases were discovered that were initially missed. Conclusions: The study reflects the potential benefits of the Paige Prostate system in the hands of experienced pathologists and validates the algorithm in a completely independent dataset. Paige Prostate can improve pathologists' sensitivity when reviewing digitized H&E prostate needle biopsy images with a minor impact on specificity.
The association of adaptive early phase study design and late phase study results in oncology using clinicaltrials.gov data.
Authors: Levy Donna Elise , Banerjee Srikanta , Tabung Fred K 
Published: May 2020

e14078 Background: Traditional methods such as the 3+3 design developed in the 1940s continue to be used in the majority of early phase oncology studies. Researchers have found that the traditional methods identify the appropriate dose level only 30% of the time. Patients are also exposed to sub-therapeutic doses due to the conservative methods. With these limitations on traditional design options, innovative methods need to be developed, adopted and assessed. Methods: This quantitative study assessed the association of the design methods (adaptive versus traditional) used for early phase oncology studies (adaptive versus traditional) and the outcome of late stage clinical trials. Differences by cancer type and by drug classification were also assessed. A sample of studies for this analysis was extracted from the National Institute of Health Clinical registry and results database. The data used for the analysis was extracted by the Clinical Trials Transformation Initiative (CTTI) Aggregate Analysis of ClinicalTrials.gov (AACT). Results: When assessing study design and outcome, there were lower odds of a positive outcome when adaptive methods were used though this association was not statistically significant (OR [95% highest posterior density (HPD)]:0.66 [0.20, 1.21]). Among the different drug types, using adaptive compared to traditional methods was associated with significantly higher odds of a positive outcome for taxanes, OR: 2.75, 95% HPD: 1.01, 5.16) and other, OR: 3.23, 95% HPD: 1.58, 5.46) but no association among studies of monoclonal antibodies or protein kinase inhibitors. There were no significant associations between early phase study design and outcome in late phase studies by cancer type (lung, breast, other). Conclusions: While results associated with the use of adaptive methods were not significant, further research should be conducted using all completed oncology clinical trials in the database to more precisely determine the relationship between adaptive study design in early phase oncology studies and outcomes in late stage studies. In addition, improvements in traditional versus adaptive design capture in the ClinicalTrials.gov database needs to be considered.
The landscape of gastrointestinal oncologic phase III clinical trials in the last two decades.
Authors: Abi Jaoude Joseph , Kouzy Ramez , Mainwaring Walker , Lin Timothy , Miller Austin B. , Jethanandani Amit , Espinoza Andres F. , Ludmir Ethan B. , Taniguchi Cullen M. 
Published: May 2020

e14087 Background: Gastrointestinal (GI) cancers are among the most common cancers in adult patients. The prognosis of GI cancers differs widely between specific subsites, with some cancers having excellent prognoses, and others (such as pancreatic and hepatobiliary cancers) having an extremely poor prognosis. We sought to characterize the landscape of GI oncologic clinical trials in the last two decades. Methods: We searched ClinicalTrials.gov for phase III RCTs between 2003 and 2018. Randomized multi-arm trials assessing a therapeutic intervention in adult cancer patients were included. GI cancer trials were identified and grouped into specific disease subsites. Differences in trial factors between GI and non-GI trials and between different GI subsites were analyzed. Results: Out of the 600 RCTs identified, 76 trials assessed gastrointestinal cancers (12.7%). By disease subsite, we identified 21 (27.6%) gastro-esophageal cancer trials, 12 (15.8%) pancreatic cancer trials, 11 (14.5%) hepatobiliary cancer trials, and 32 (42.1%) colorectal cancer trials. GI cancer trials were more likely to be industry funded when compared to non-GI cancer trials (88.2% vs 76.3% respectively, p = 0.02). Furthermore, only 18.4% of GI trials had cooperative group sponsorship (vs 33.3% for non-GI trials, p = 0.01). Most GI cancer trials assessed systemic therapy interventions (85.5%); only 6 trials (7.9%) addressed supportive care interventions (vs 20.2% for non-GI trials, p = 0.01). Around half of GI cancer trials (51.3%) met their primary endpoints, but only 8 trials (10.5%) led to subsequent FDA approval. All FDA approvals were for colorectal cancer treatments, and none for gastroesophageal, pancreatic or hepatobiliary cancers (p = 0.01). Most patients (97.1%) accrued on GI cancer trials had a good performance status (Eastern Cooperative Oncology Group of 0 to 1). Specifically, 95.9% of patients in pancreatic trials and 98.6% of patients in hepatobiliary trials had a good performance status. Conclusions: While many GI cancers have a poor prognosis, very few trials assess supportive care interventions in this populations. Moreover, most patients included in GI cancer trials have a good performance status. In that regard, future GI cancer trials addressing supportive care measures with a wider cancer population are still warranted.
Transforming the site feasibility assessment process for oncology clinical trials.
Authors: Kurbegov Dax , Hurley Patricia A. , Waterhouse David Michael , Nowakowski Grzegorz S. , Kim Edward S. 
Published: May 2020

e14092 Background: Current methods to assess trial sites for clinical trial participation are onerous, with unnecessary redundancies and no-value steps that impact research site resources and clinical trial participation. This project sought stakeholder feedback on recommendations to transform industry sponsor and contract research organization (CRO) processes for evaluating sites for trials. Methods: An ASCO task force developed recommendations to improve the feasibility assessment process and standardize and centralize questions and forms. A survey was conducted with sites, industry trial sponsors, and CROs to obtain feedback and assess buy-in for the recommendations. Results: Respondents were from 28 oncology research sites (19 academic, 9 community-based), 8 sponsors, and 4 CROs. All stakeholders agreed that the current process is burdensome (93% sites, 90% sponsors, 100% CROs), standardization will improve the process (86% sites, 87% sponsors, 75% CROs). All agreed a centralized portal will reduce burdens (93% sites, 100% sponsors, 75% CROs) and expedite trial start-up (89% sites, 100% sponsors, 75% CROs). Site certification was a viable option for sites (86%) and CROs (75%), but less so for sponsors (57%). Most respondents preferred a two-tier model: 1) a short site questionnaire followed by a pre-study visit for new interactions, and 2) only a pre-study site visit or a teleconference if there is an existing relationship. The greatest benefits were time savings, expedited start-up, reduction in personnel resources, and cost savings. The greatest barriers to adoption were buy-in and alignment from sponsors/CROs and insufficient information about site or protocol. Top predictors of a site’s success on a trial were physician engagement, available patients, and site experience. Conclusions: Site feasibility assessments are important for all stakeholders to establish trial suitability. However, current methods impose tremendous burdens on site resources (reported by authors elsewhere). While this sample is limited, the proposed process and standardization changes show promise to reduce burdens and costs for all stakeholders and expedite patient enrollment onto clinical trials.