bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of a deep convolution neural network (DCNN) for osteosarcoma necrosis quantification and outcome prediction.
Authors: Occidental Michael , Coudray Nicolas , Tsirigos Aristotelis , Jour George 
Published: June 2023

e13541 Background: Deep convolutional neural network is a form of artificial intelligence that gained traction recently in diagnostic and prognostic applications such as automated tissue segmentation and outcome and response to immunotherapy prediction. Herein, we optimize DCNN to accurately segment and quantify necrosis in osteosarcoma (OSA) archival slides. We also correlate the AI driven necrosis ratios with those derived from experienced sarcoma pathologists (gold standard) and show their value in predicting patient’s outcomes. Methods: A total of 44 patients with bone resections for primary high-grade osteosarcomas post neoadjuvant treatment with conventional chemotherapy were included (Male n= 26, female n=19), median age = 16.4 years. 103 slides from 21 patients were originally manually segmented and used for initial training using 3-fold cross-validation split per patient and per slide. Here, an additional 119 slides from 23 patients (total of 222 slides) were added in the retraining for automated segmentation and to update the necrosis ratio calculation, and the correlation with pathologist calculated necrosis ratio (R spearman correlation). Based on this ratio, we performed a prediction of Overall survival using the Log rank test. Results: The segmentation classifier identified viable tumor, necrosis, normal tissue and artefacts within slides with an area under the curve ranging from 0.91 to 0.96. AI necrosis ratio showed a good correlation with pathologist driven ration (R=0.74 per slide, R=0.69 per patient). Some outliers with highest discrepancy for necrosis ratio included surface high grade periosteal osteosarcomas (known for cartilaginous differentiation that is not part of the original training of the algorithm). Patients with an AI necrosis ratio of ≥ 70% had a better Overall survival at 36 months (p=0.01). Conclusions: Our results show the potential of integration of DCNN for automated segmentation and necrosis quantification of central high OSA resection enhancing efficiency in interpreting these complex surgical specimens. The data also suggests a possible prognostic application of the AI derived necrosis ratio. Further validation of the DCNN performance on an independent cohort is underway.
Predicting which cancer patients will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.
Authors: Nunez John-Jose , Leung Bonnie , Ho Cheryl , Ng Raymond T. , Bates Alan 
Published: June 2023

e13544 Background: Patients with cancer often have unmet psychosocial needs. Early detection of who might require referral to a counsellor or psychiatrist may help improve their care. In this work, we investigated whether natural language processing (NLP) can predict which patients will see a counsellor or psychiatrist from a patient’s initial oncologist consultation document. NLP is the branch of artificial intelligence that uses language models to accomplish tasks using written text. A different application of NLP has recently been popularized through the ChatGPT online question-answering system. In this work, we used traditional and neural language models to predict whether patients will see a psychiatrist or counsellor based on their initial oncologist consultation document. Methods: This retrospective prognostic study used data from 47,625 of 59,800 patients who received initial cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011 and December 31, 2016. We excluded patients with multiple cancer diagnoses, or those that did not see a medical or radiation oncologist within 180 days of their cancer diagnosis. We trained neural language models to predict which patients would see a psychiatrist or counsellor in the twelve months following their initial oncologist consultation. We used the non-neural language model bag-of-words, as well as three neural models: convolutional neural networks (CNN), long-short term memory, and bidirectional encoder representations from transformers. Results: The models performed similar to or better than previous applications of artificial intelligence in predicting psychosocial needs. Our best-performing models, utilizing CNNs, achieved a balanced accuracy above 75% and a receiver-operator area-under-curve (AUC) above 0.80 when predicting which patients would see a psychiatrist. Performance was lower for predicting which patients would see a counsellor, with balanced accuracy and AUC exceeding 70% and 0.75, respectively. We examined what words or phrases our models used to make the predictions, finding symptom burden, certain cancers, and family history of cancer were positive predictors of future referral to psychosocial services. Conclusions: Our results suggest NLP can be used to predict which cancer patients require referral to a psychiatrist or counsellor from their initial oncologist consultation. Future research may be able to extend our work to predict other needs of cancer patients such as palliative care, or other cancer outcomes more generally.
Effect of an artificial intelligence–powered programmed death-ligand 1 combined positive score analyzer in urothelial cancer on inter-observer and inter-site variability.
Authors: Cho Soo Ick , Park Jeong Hwan , Lee Kyu Sang , Choi Euno , Jung Wonkyung , Song Sanghoon , Kim Sukjun , Shin Jisoo , Ryu Jeongun , Valero Puche Aaron , Brattoli Biagio , Park Seonwook , Paeng Kyunghyun , Ock Chan-Young 
Published: June 2023

e13546 Background: Programmed death-ligand 1 (PD-L1) is a predictive marker for immune checkpoint inhibitors treatment response in urothelial carcinoma (UC). The combined positive score (CPS) is a representative method to evaluate the expression level of PD-L1 in UC. However, inter-observer and inter-institute variations can disrupt accurate CPS evaluation. The purpose of this study is to assess the role of an artificial intelligence (AI)-powered PD-L1 CPS analyzer on UC in reducing inter-observer and inter-institute variability. Methods: Lunit SCOPE PD-L1 CPS was developed with 4.94 x 105 tumor cells and 4.17 x 105 immune cells from 360 PD-L1 stained whole-slide images (WSIs) of UC from multiple institutions. 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. Three uropathologists from different university hospitals evaluated the CPS classification (≥10 or <10) of 543 PD-L1 stained WSIs of UC from each hospital. The result with concordant CPS classification of each slide across ≥ 2 pathologists was considered the consensus. Each pathologist revisited to evaluate WSIs by referencing the AI model inference, after a washout period, if there was a discrepancy between the pathologist and the AI model. Results: Of 543 WSIs, 446 (82.1%) were classified as the same CPS subgroup by all three uropathologists. Also, pathologists had a high degree of concordance with the consensus in WSIs from their own hospitals. They re-evaluated 64, 73, and 75 WSIs with AI assistance, respectively, and changed the CPS classification for 47, 48, and 48 WSIs. After re-evaluation with AI assistance, three uropathologists agreed on the same CPS classification in 510 WSIs (93.9%). The overall percentage agreement (OPA) of each pathologist with the consensus increased from 95.0%, 94.8%, and 92.3% to 98.7%, 98.3%, and 96.9% after AI assistance, and the OPA for WSIs of other institutions increased more compared to the OPA for WSIs of their own hospital. Conclusions: This study shows that an AI-powered PD-L1 CPS analyzer in UC can reduce inter-observer and inter-site variability. This result suggests that the AI model will help evaluate CPS in UC more accurately and reduce variation in situations where pathologists analyze WSIs from unfamiliar institutions.[Table: see text]
Characteristics and predictive modeling of HER2 mutation in patients with non-small cell lung cancer.
Authors: Varolgüneş Uras , Mittal Garima , Parikh Kaushal , Ip Andrew , Malhotra Jyoti , Bates Benjamin , Salas Maribel , Arunachalam Meena , Chen Haoqian , Chen Wenjin , Yu Dantong , Setoguchi Soko 
Published: June 2023

e13559 Background: Recent advances in targeted therapies have increased the survival and quality of life for non-small cell lung cancer (NSCLC) patients. Understanding clinical characteristics and predicting HER2 status may facilitate efficient use of next-generation sequencing (NGS) testing and promote precision medicine/targeted therapies. Methods: NSCLC patients ≤65 years old, diagnosed between 01/2011 and 06/2021 who have NGS test results were identified in from two National Cancer Institute designated cancer centers in the Northeastern region of the United States. Demographics, behavioral, and clinical characteristics were extracted from electronic health record (EHR) data and EHR-linked cancer registry. Prevalence prediction modelling of HER2/ERBB2 mutation was performed using machine learning (ML) methods including logistic regression (LR), random forest (RF), naïve Bayes (NB), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP). 10- fold cross validation and k-nearest neighbor method were used to impute missing values. Under-sampling, oversampling and feature selection methods were tried. Results: Among 761 NSCLC patients with NGS reports available, 353 (46%) were ≤65 years old. Among 353 (mean age 57, 58% female, 88% non-Hispanic, 69% white, 57% had metastasis, 58% prescribed chemotherapy) 25 (7%) had a HER2 mutation. Based on univariate significance tests with LR on the complete data: cerebrovascular disease, BMI between 27.5-29.9, KRAS, EGFR and MET were significant with odds ratios (ORs) ranging from 0.2-3.9. In our prediction models, BMI, cerebrovascular disease, weight loss, hepatic metastases, diabetes, congestive heart failure, and KRAS, EGFR, MET and RET alteration status were significant predictors in multivariate analyses, suggesting the potential usefulness of these features in predicting HER2 mutation among patients with NSCLC. Area under the ROC curve (AUC) varied across different models from 70.9% (RF), 70% (XGB and MLP), 69.5% (LR) to 68.2% (Naïve Bayes). The best performing model with RF yielding an AUC ~71% can provide relative high specificity and acceptable sensitivity depending on the cut off. Conclusions: Our prediction models yielded AUC around 70% with the best performing model’s AUC being 71% in a subgroup aged ≤65. After further validation, our prediction model may be used to identify patients appropriate for repeat NGS testing who did not have HER2 testing in first molecular profiling. Future work is needed to predict status in repeat testing with previously test negative patients who relapse or have progression of disease. Random forest (RF) Model performance across different cutoff for the predicted probability. [Table: see text]
Advanced deep-learning algorithm for multi-cancer detection using cf-WGS.
Authors: Lee Tae-Rim , Cho Eunhae , Lee Junnam , Ahn Jin Mo , Kim Dasom , Ki Chang Seok , Kim Minsoo , Lee Jae Sung , Sohn Ji Hye , Park Sook Ryun , Song Ki Byung , Jun Eunsung , Oh Dongryul , Lee Jeong-Won , Park Young Sik , Song Gi-Won , Byeon Jeong-Sik , Kim Bo Hyun 
Published: June 2023

e13560 Background: Applying machine learning in circulating cell-free DNA (cfDNA) whole genome sequencing (WGS) holds the potential to be a valuable tool for early cancer detection. However, unless the batch effect is taken into account, detecting very tiny cancer signals involves an overfitting issue with relatively small training sample number for artificial intelligence training. This study aimed to develop advanced deep-learning model for cancer detection from cfDNA whole genome sequencing data with minimal batch effect. Methods: We generated low depth cfDNA whole genome sequencing data from 412 cancer patients and 1,269 healthy individuals. The size and end motif frequency of each fragment was measured and represented as a two-dimensional matrix. Each sample data was normalized with reference samples in the same batch prior to model training to remove batch effects between sequencing runs and was divided into 4 datasets stratified by sequencing batch for cross-validation of the models. The final model combines a grouped convolution neural network (CNN) and a long short-term memory (LSTM) model for sequential size information training. Results: The model showed an accuracy of 93.1% and an AUC of 0.97. With a 95% specificity threshold, the model showed an overall sensitivity of 87% and a precision of 84.7%. For individual cancer types, liver, ovarian, esophageal, pancreatic, and lung showed sensitivities of 94.4%, 81.1%, 85.6%, 100%, and 78.2%, respectively. For cancer stages, the sensitivity was 78.6% in stage I, 86.5% in stage II, 91.5% in stage III, and 92.7% in stage IV at 95% specificity threshold. Conclusions: The deep learning models trained using fragment size and end motif frequency of cfDNA demonstrated promising results for cancer detection. The improved performance of this model highlights the potential for improving cancer detection by incorporating advanced deep learning algorithm with well curated training data for batch effect correction.
Deep learning-based prediction model for predicting the tumor origin of cancers of unknown primary.
Authors: Lan Lan , Zhang Yuman , Li Xing , Wang Kai 
Published: June 2023

e13562 Background: Identifying the origin of tumors is important for the treatment and prognosis of cancer patients. Despite of routine examinations including medical history, laboratory tests, histopathological assessments and imaging, 3-5% of metastatic cancers are diagnosed with cancers of unknown primary (CUP). In clinical practice, the management of CUP is challenging and empirical chemotherapy remains to be the main treatment option, leading to poor prognosis. Methods: To predict the tumor origin, we built a weighted ensemble model by Automated machine learning (autoML), including 13 models, using molecular and clinical datasets consisted of 22,018 patients and 28tumor types. The tumor type of each patient was confirmed by at least 3 pathologists. Our molecular and clinical datasets contained 7 features including age, tumor site, gene alteration, tumor mutation burden (TMB), microsatellite instability (MSI), etc. Features selected by AutoML were used to train the diagnostic model by using algorithms including CatBoost, ExtraTreesEntr (Extremely randomized trees), ExtraTreesGini, KNeighborsDist, KNeighborsUnif, LightGBM, XGBoost, etc. The best model was selected by autoML. We then optimized the model parameters and developed a No-Bias algorithm to ensure that the trained model can output "fair and unbiased" prediction results even when the input dataset had an uneven distribution in the number of cases for each cancer type. 5-fold cross validation was used to estimate the accuracy of the diagnostic models. Moreover, we validated the accuracy of diagnostic model using an independent validation set of 1,636 patients covering 28 tumor types. Results: Our model outputs the most confidently predicted tumor types in the form of TOP N (N=1-5). In the training set, the accuracy of TOP1, TOP3, and TOP5 of our diagnostic model was 72.8%, 88.4%, and 93.3%, respectively. had been successfully applied to support the diagnosis of 10 CUP patients. In one patient who was predicted to have esophageal squamous cell carcinoma, immune checkpoint inhibitor combined with chemotherapy regimen was given and achieved a 9-mouths PFS. Conclusions: We successfully developed a deep learning-based diagnostic model for predicting tumor origin with genomic and clinical data. The model could be applied to improve the clinical diagnosis of CUP patients and guide the treatment decision according to the predicted tumor origin.
AI-driven real-time patient identification for randomized controlled trials.
Authors: Miyasato Gavin , Kasivajjala Vamsi Chandra , Misra Mohit , Kumar Kiran , Kadam Amrut Sadashiv , Friedman Howard S 
Published: June 2023

e13565 Background: In India, clinical trial recruitment is a challenging process, often relying on the time- and labor-intensive process of investigators reviewing handwritten charts to identify potential candidates. We developed an AI-driven, real-time solution to address this issue and demonstrated its feasibility by identifying patients that would have been candidates for a 2017-2018 study of metastatic breast cancer (mBC) patients who failed prior chemotherapy. The original trial recruited 46 patients across 16 sites over 7 months in India. Methods: The study protocol was reviewed and applied to six years (2017-2022) of retrospective electronic medical records (EMR) data from a cancer hospital in India’s National Capital Region. Trial inclusion and exclusion criteria were translated to data elements which were extracted from both structured (e.g., lab) and unstructured (e.g., clinical notes) EMR data. For the unstructured data specifically, natural language processing algorithms were developed and implemented over the span of three weeks to extract the specific data elements. The veracity of the extracted data was confirmed via manual review by an oncologist. To assess the sensitivity of the results to the study criteria and provide a range of the potential candidate population size, the criteria were applied in two ways – as a strict application (e.g., ECOG must be reported and must be < = 2) and as a relaxed application (e.g., ECOG may be unreported or, if reported, must be < = 2). Results: Among the initial cohort of 112,239 females between 18 and 65 years of age, 3,203 (3%) were identified as having mBC and failure of prior chemotherapy or relapse. 537 patients remained after applying all inclusion criteria (ECOG status < = 2, LVEF > = 50%, no prior radiotherapy, prior chemo completed 4+ weeks prior, adequate bone marrow, renal, and hepatic function). Exclusion criteria (prior taxane exposure, 2+ prior chemotherapies, cardiac conditions, CNS lesions, motor/sensory neurotoxicities, HBV/HCV/HIV infection) were then applied, resulting in 37 patients that would be potential candidates for the trial, per the strict application of criteria. The relaxed version of the analysis yielded a candidate pool that was five times larger than the strict application, which was largely driven by cases where information was unavailable in the EMR. Conclusions: We demonstrated that the use of an AI-driven platform to apply clinical trial criteria to Indian real-world data can provide an efficient solution to identifying candidate trial patients. Once the criteria are defined and algorithms are developed, patient identification can happen in real time and reduce patient recruitment timelines by months. Future applications will include other cancer types and expand the breadth of data elements extracted to enable the rapid assessment of additional inclusion and exclusion criteria.
Clinical factors associated with successful liquid tumor biopsy in men with prostate cancer.
Authors: Guo Aixia , Valle Luca Faustino , Ahmed Sara , Rowe Kerry G. , Pritchard Colin C. , Montgomery Robert Bruce , Garraway Isla , Nickols Nicholas George , Maxwell Kara Noelle , Kelley Michael J. , Rettig Matthew 
Published: June 2023

e13567 Background: Liquid biopsies using cell-free DNA (cfDNA) can be used in prostate cancer (PrCa) patients when tissue is unavailable and repeat biopsy is not feasible. Given the older age of PrCa patients, liquid biopsy may detect alterations related to clonal hematopoiesis of indeterminate potential (CHIP), generating uncertainty in the clinical utility of the results. We sought to identify clinical predictors of successful cfDNA biopsy, where the reported alterations are more likely to reflect the individual’s prostate cancer cfDNA instead of CHIP in a large cohort of United States Veterans with PrCa. Methods: Next-generation sequencing of cfDNA biopsy specimens was performed through the VA National Precision Oncology Program (NPOP) from February 2019 to November 2022. Successful identification of PrCa cfDNA testing was defined as the identification of an alteration in one or more PrCa-related genes included on the panel ( AR, CDK12, SPOP, MED12, CCND1, BRAF, AKT1, TMPRSS2, ERG, ETV1, and ETV4). Student’s t-tests, univariate, and multivariate logistic regression estimated significant patient and disease-specific factors at the time of cfDNA biopsy and the likelihood of obtaining a successful cfDNA biopsy result. Results: Among 5611 total cfDNA tests performed, 2066 cfDNA tests from 1985 Veterans listed PrCa as the clinical indication, passed quality control measures, and could be linked to patient-level demographics. 814 (39%) tests were deemed successful based upon identification of at least one PrCa related gene alteration. The most frequently encountered PrCa alterations were in AR (76.5%) and TMPRSS2 (22.4%). PSA was significantly higher (IQR 209.1 ng/ml) and PSA doubling time (PSADT) was significantly shorter (IQR 2.4 months) in the cohort of Veterans with successful cfDNA testing (p<0.001). Among 1581 tests that reported tumor fraction, an elevated tumor fraction >10 was also significantly associated with success (46.3% vs 8.1%, p<0.001). On multivariate analysis, PSA elevation above 4.8 ng/ml (p<0.001) and PSADT <6 months (p<0.001) were significantly associated with successful cfDNA testing. Actionable DDR mutations, including ATM (15.2% vs 11.7%, p=0.02), BRCA1 (1.7% vs 0.6%, p=0.03), and BRCA2 (7.4% vs 4.4%, p=0.01) were significantly more likely to be detected in a successful test compared to an unsuccessful test. Conclusions: High PSA and short PSADT are significant predictors of a cfDNA test identifying PrCa-related gene alterations in Veterans with metastatic disease instead of alterations due solely to CHIP. Liquid biopsy in PrCa should be reserved for patients with relatively higher disease burden or more aggressive growth rate, reflected by PSA values > 4.8 and PSADT < 6 months, to increase the likelihood of identifying actionable alterations and improve interpretation of testing.