bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying care fragmentation in the management of non-metastatic muscle-invasive bladder cancer.
Authors: Bhattacharya Manisha , Sun Zhaojun , Jacobs Bruce Lee , Yabes Jonathan G. , Sabik Lindsay M.
Published: June 2023

e13527

Background: Measuring care fragmentation facilitates exploration of breakdowns in cancer care delivery that may impact quality of care and patient outcomes. Bladder cancer often requires a multimodal approach of surgery, chemotherapy and radiation, increasing the risk of fragmented care. This study assesses the extent of care fragmentation across different inpatient facilities and examines patient characteristics associated with inpatient care fragmentation. Methods: We adapt the modified Herfindahl-Hirschman market concentration index to construct a Fragmentation Index (FI) capturing the distribution of a patient’s care across inpatient facilities. We calculate FI for the 15 months following diagnosis for non-metastatic muscle-invasive bladder cancer (MIBC) patients who underwent radical cystectomy from 2010-2016 and had at least 2 admissions during the study period. We use Pennsylvania Cancer Registry data linked to statewide facility discharge records to estimate patient-level multivariable regression models of the relationship between FI and patient demographics, disease features, rurality, area deprivation index (ADI), payer, and Commission on Cancer hospital (CoC-H) accreditation status. Results: Of 965 patients, 87.3% (n = 842) lived in urban areas, 8.9% in large towns and 3.8% rural. Approximately 8% had Medicaid and 5.7% had uninsured or unknown payer status. A majority (64%; n = 614) were age 60-79; 76% were men and 92% non-Hispanic White; 19.5% (n = 188) had T4 disease and 25% (n = 239) received neoadjuvant chemotherapy. Almost half of patients (45.3%; n = 437) had an FI of 0, indicating all admissions were at the same hospital. The mean FI was 0.254 for the total cohort and 0.464 for patients with FI > 0. Residency in a large town or rural area was associated with 0.59 (CI 0.003—0.114) and 0.14 (CI 0.060—0.220) higher FI relative to urban areas. T4 disease was associated with 0.063 higher FI (CI 0.019—0.107) relative to <T2 disease. Factors associated with lower FI included Non-White race (-0.092, CI -0.151 – -0.033) and no admissions to CoC-H (-0.138, CI -0.182 – -0.095) relative to patients admitted to at least 1 CoC-H. Patients never admitted to a CoC-H (n = 134) mostly lived in urban areas (88%) and 49% had Medicare insurance: they were disproportionately from the most disadvantaged ADI quartile in the cohort (37.3%). Conclusions: Our findings highlight that many MIBC patients receive inpatient care across at least two different hospitals. Advanced disease and patient residence in non-urban areas are associated with greater fragmentation of inpatient care. Patients who never received care at a CoC hospital experienced less fragmentation but were more likely to be relatively economically disadvantaged. Future research will investigate relationships between fragmentation and clinical outcomes to inform policy and clinical strategies aimed at optimizing patterns of care.
Assessing the feasibility of recording smartphone-based patient-reported outcomes in patients with cancer: A pilot study.
Authors: Batra Atul , Sharma Atul , Bakhshi Sameer , Gogia Ajay , Pramanik Raja , Khurana Sachin , PUSHPAM DEEPAM , kumar akash , Sharma Aparna
Published: June 2023

e13528

Background: We conducted this study to evaluate the feasibility of completing patient-reported outcomes (PROs) using a mobile-based secure system at home, as opposed to the traditional method of completing them in crowded outpatient oncology clinics during hospital visits, which may not be ideal in resource-limited settings. Methods: The study included patients aged over 18 years who were newly diagnosed with solid-organ cancer between July 2021 and July 2022 at a tertiary cancer center in India. Patients who were able to use a smartphone were invited to complete a mobile-based Edmonton Symptom Assessment Scale (ESAS) questionnaire, which was accessed via a secure link sent to their phone as a chat. The ESAS questionnaire included physical (six domains: pain, tiredness, drowsiness, shortness of breath, nausea, loss of appetite), psychological (anxiety and depression), and overall well-being (one domain) questions, each rated on a scale of 0-10, with a higher number indicating greater symptom burden. Symptoms were classified as mild (1-3), and moderate to severe (4-10). The primary objective was to determine the completion rate of the questionnaire, while the secondary objectives were to determine the incidence of moderate to severe symptoms, both physical and psychological, at cancer diagnosis. Multivariate logistic regression analysis was used to identify factors associated with moderate to severe symptoms. Results: We reached out to 707 consecutive patients who had recently been diagnosed with solid-organ cancer and used smart phone. The median age of participants was 53 years (with an interquartile range of 43-62 years), and 52.3% were female. Breast cancer (30.6%) was the most common diagnosis, followed by lung cancer (28.8%). Approximately, one-third of all patients had metastatic disease at diagnosis, while others were similarly distributed in stage I-III. Overall, 650 patients (91.9%) patients completed the mobile-based questionnaire; 38.9% of patients had moderate to severe physical symptoms and 30.9% had moderate to severe psychological symptoms. Pain (57.7%) and tiredness (58.7%) were the most commonly reported physical symptoms in moderate to severe category, while nausea (18.1%) and drowsiness (20.3%) were reported least frequently as moderate to severe. Anxiety (35.1%) was more prevalent than depression (26.1%). The total symptom score was mild in 76.5% of patients and moderate to severe in 18.8%. On multivariate logistic regression, patients with advancing age, female sex, and metastatic disease at diagnosis were more likely to report moderate to severe symptoms. Conclusions: Using smartphone-based PROs can be an efficient way to record symptoms in cancer patients, and their high completion rates make them suitable for routine use in oncology clinics, especially considering the increasing number of smartphone subscribers globally.
The availability of comprehensive care of gastric cancer in developing countries: Georgian experience.
Authors: Esakia Tamar , Abuladze Mariam , Maglakelidze Marina , Tsiklauri Khatuna , Sharikadze Nino , Cholikidze Tamta , Rukhadze Tamari
Published: June 2023

e13529

Background: The availability of comprehensive cancer care in different regions is the main problem in developing countries. In fact, the cancer centers are centralized and patients fail to receive the proper medical care in regions. The situation is critical for patients living in the zones of conflict. In Georgia we have cancer centers only in two cities Tbilisi and Kutaisi. The aim of the study was to investigate the rate of gastric cancer diagnoses in different regions of Georgia, that could be a model for developing countries. Methods: The spread of gastric cancer in Georgia was investigated retrospectively by analyzing the cases reported in different regions based on 5 years national cancer registry data from 2015 to 2021. The data included the number of cases, age, sex, and stage of gastric cancer. Data from all regions of Georgia were analyzed including occupied regions as Abkhazia and Tskhinvali. Other regions were – Tbilisi, Adjara, Guria, Imereti, Kakheti, Mstkheta, Racha, Samtskhe, Kvemo Kartli, Shida Kartli. Results: A total of 1825 patients with gastric cancer were evaluated during the period of 2015-2021 years. The highest number of diagnosed cases were reported in Tbilisi, with 258 cases in males and 200 cases in females, followed by Imereti (Kutaisi) with 125 cases in males and 80 cases in females and Adjara 132 cases in males and 56 cases in females. The other regions had lower numbers of diagnosed cases, with the lowest being Tskhinvali with only three cases in males and six cases in females. In Abkhazia were 44 cases in males and 19 cases in females. In terms of stage distribution there was no difference between the regions, the majority of patients were stage IV accounting 42 % of all cases, 28% was stage III, 10 % was stage II, 5% was stage I and 15% was of unknown stage. Age ranges were different according to the sex and regions – in Tbilisi 35-83 in males and 26-94 in females, in Adjara 29-88 in males and 28-84 in females, in Abkhazia 35-83 in males and 41-83 in females, in Guria 37-91 in males and 39-83 in females, in Imereti 37-88 in males and 34-84 in females, in Kakheti 34-85 in males and 37-89 in females, in Mtskheta 34-91 in males and 50-84 in females, in Racha 31-72 in males and 30-81 in females, in Samegrelo 33-96 in males and 21-90 in females, in Samtskhe 43-94 in males and 38-81 in females, in Kvemo Kartli 26-85 in males and 38-90 in females, in Shida Kartli 39-85 in males and 38-90 in females, in Tskhinvali 66-84 in males and 33-76 in females. Conclusions: The study found that availability of comprehensive care for gastric cancer including diagnosing is limited in the regions of Georgia, with the lowest rates in occupied regions. To improve the cancer care in developing countries such as Georgia international and national strategies are needed.
Addressing suicidal ideation in oncology patients.
Authors: Sandow Lyndsey , Friedman Ryan , Agrawal Neha , Graff Julie N. , Thawani Rajat
Published: June 2023

e13534

Background: The diagnosis of cancer leads to worsening a patient’s physical, mental, personal stressors and inevitably can lead to patients questioning their mortality. Cancer is the leading cause of death in the United States, and the diagnosis and treatment of cancer can be very difficult for patients from a physical, psychosocial, and financial perspective. Suicide is the 10th leading cause of death in the United States and risk factors for suicide include family history of suicide, previous suicide attempts, feelings of hopelessness, and depression. In addition to these important risk factors, a recent study including 8.6 million cancer patients showed that risk of suicide is four times greater in cancer patients than the general population. Despite this elevated risk, the detection of cancer patients at highest risk of suicide is poor and there are limited resources to help providers risk stratify cancer patients at highest risk of suicide. Methods: We conducted a cross sectional study of Portland VA oncology providers (physicians, housestaff, advanced practice providers, nurses and social workers) to determine the frequency in which those providers work with oncology patients struggling with suicidal ideation, the frequency of screening completed by the oncology providers, and the barriers to screening patients for suicidal ideation during a regular office visit. Qualtrics surveys were sent to all VA oncology providers. A significance test for proportions was utilized to determine the statistical validity of the results. Results: A total of 22 providers completed the survey. The majority of respondents were MD/DOs (n=13 We showed that most providers have cared for patients affected by suicidal ideation including attempted suicide and completed suicide attempts (p < 0.05). While most providers answered that they had screened patients for suicide before all respondents noted significant barriers to screening patients. The majority of respondents identified multiple barriers to addressing suicidality with almost all respondents included “not enough time” as one of the barriers. Other prominent barriers included, “unsure of next steps”, “not having proper tools to assess”, “not having expertise to assess”. Conclusions: Our study showed that most oncology providers will care for patients affected by suicidal ideation during their career. In addition it shows that while many providers have screened patients for suicidal ideation, most providers face significant barriers to doing so during a clinic visit. Our study contributes to the current body of knowledge by identifying the significant barriers faced in the providers in the Portland VA oncology clinic.
A low-cost breast cancer prognosis tool for developing countries using AI.
Authors: González Espinoza Iván Romarico , Castro-Ponce Abraham , Jiménez-Monferrer Sergio , Castelló-Pons María , Cardona-Ferrer Edgar E. , Juárez-Salazar Gabriela , Juárez-Salazar Diego A. , Montes-de-Oca-Moreda Ricardo I. , Coyotl-Huexotl Marisela , Chiquillo-Domínguez Mariana , Acosta Ponce de León Eric , Aguilar Priego José Manuel , Cordero Vargas Carlos , Cortes Garcia Carlos Alberto , Ceja-Utrera Francisco J. , Lara-Bejarano Juan C. , Gonzalez Blanco María de Jesús , Garibay Diaz Julio Cesar , Aranda Claussen Jorge E.
Published: June 2023

e13536

Background: The use of artificial intelligence in breast cancer diagnosis has had a significant impact. Alternative to other tests, machine learning (ML) models are low-cost tools that can establish an objective prognosis by considering all the clinical, genomic, and histological data from patients. To evaluate the performance of 12 ML models in predicting the overall survival of breast cancer patients at 60 months of follow-up. Methods: Clinical data was obtained from “The Cancer Genome Atlas-Breast Cancer" database. Preprocessing of data ruled out variables with insufficient values or poor relevance. Data was divided into an 80/20 ratio to perform the training and testing of the models. The models used were: Logistic Regression (LR), Ridge Classifier (RC), Least Absolute Shrinkage and Selection Operator (LASSO), K-nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Tree (DT), Multilayer Perceptron (MP), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). Initial training used the default hyperparameters of each model, performance was evaluated through a 5-fold-cross-validation, the results were used with the GridSearchCV tool to optimize the hyperparameters for the final training and testing of the models. Results: The models with most accuracy were NB (86.76%) and SGD (85.29%), with most sensitivity were: SGD (93.75%), MP (91.67%) and NB (89.89%), with the most specificity were: KNN (95.0%), DT (90.0%) and LASSO (85.0%), with the highest area under the ROC curve were: LDA (90.83%), LASSO (90.1%), and LR (89.79%). The variables with the highest relevance were: "Previous diagnosis of cancer", "Presence of tumor", "Ancillary Therapy", and "Histology". Integrating the best-performing models into an interactive tool preserves the best features without sacrificing efficiency. Conclusions: The models with the best overall performance in predicting the prognosis of patients with breast cancer were NB and LASSO. The best way to evaluate the performance of a predictive prognosis tool is the area under the ROC curve. For developing countries, an AI tool to predict the patient outcome is viable when other expensive prognosis genomic tools are not available. Further studies with data from developing countries are needed to improve the performance of this tool.
Guidance on modeling circulating miRNA to distinguish multiple cancer types by an observation of large-scale open data.
Authors: Hsieh Jason Chia-Hsun , Hsieh Tsung-Ting , Lee Ko-Han , Chang Yu-Chuan
Published: June 2023

e13537

Background: Cell-free miRNAs (cf-miRNA), circulated in body fluids such as plasma or serum, have shown their ability to detect, diagnose, and monitor cancers. Combining machine learning (ML) technology with these biomarkers facilitates early detection of cancers, which increases the accuracy of clinical decisions and empowers people to take control of their health status. However, the data of the cf-miRNAs has characteristics, which will affect the results of ML. Therefore, this study tries to expound on them in different aspects and to build a reasonable model. Methods: We downloaded large-scale datasets of the platform GPL21263 from the Gene Expression Omnibus for modeling experiments. We curated 8,174 subjects with 2,565 miRNA targets across 7 cancer types of different cf-miRNA-based cancer studies. Moreover, we used principal component analysis (PCA) to observe the datasets, recursive feature elimination (RFE) for feature selection, and tree-based algorithms to build the prediction model. Results: The characteristics of the cf-miRNA we like to share are: (1) Cancer subjects express more cf-miRNAs than control subjects. In the control group, there were 294 and 327 miRNAs with missing rates under 50% and 25%, respectively. In contrast, there were 395 and 485 miRNAs with the same thresholds in the cancer group; (2) Dividing subjects into cancer and control groups is simpler than distinguishing specific cancer types. In the PCA, the average Euclidean distance between the control group and each cancer type is 98.48, while it is 20.23 within each cancer type; (3) For obtaining cancer-specific biomarkers, we suggested that other non-target cancer subjects should be considered as negative controls. We modeled 7 cancer types and compared the proportion of cancer-specific biomarkers, not selected by any other models. The proportion increased from 30.0% to 57.8% after we added other non-target cancer subjects to the control group; next, we focus on multi-cancer modeling: (4) We need at least 400 samples to distinguish seven cancer types. In our experiment, we kept increasing the size of the training data, a hundred at a time. As data was added to the model, the accuracy increased but plateaued after adding 400 samples; (5) Based on RFE, 120 miRNAs is a reasonable number to distinguish seven cancers. Moreover, we found some of these miRNAs are only expressed in cancer subjects. We might lose this kind of biomarkers if we filtered out them by the missing rate; (6) The 10-fold cross validation accuracy of the multi-cancer model can achieve 93.0% using the gradient-boosted trees algorithm. Conclusions: In this study, we showed the guidance for modeling miRNAs in different aspects, including labeling strategy, sample and feature sizes, and the high-accuracy multi-cancer model we can achieve. We hope this guidance would inspire researchers on cf-miRNA-related machine learning applications.
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.
Time savings from integrating discrete, genomic data into the electronic health record using the Epic Aura Network.
Authors: Posteraro Leo , Mergler Patrick , Urbanek Dennis , Schulin Joseff , Anton Howard , Kokkinos Nancy , Chen James Lin
Published: June 2023

e13568

Background: The capability to deliver, order, and receive discrete genomics data from external pathology laboratories into the electronic health record (EHR) has improved dramatically. As opposed to unstructured PDF integrations, discrete genomic results allow physicians real-time receipt of results which may trigger rapid clinical actions and expedite patient care. However, these genomic integrations, traditionally point-to-point (P2P), can be time consuming. On the other hand, Epic’s order and results network (Aura) connects pathology labs directly to Epic, which has pre-existing connections to Epic EHR sites. This eliminates the need to create a new connection, and is designed to provide a faster and simpler approach. Tempus was the first vendor to use the Epic genomics module for delivery of discrete, somatic mutational data and now is also the first company to leverage Aura for this purpose. Here, we report on our early Aura integration experiences. Methods: Using the Tempus Database, we retrospectively analyzed medical systems that were all Epic customers and determined their EHR genomic integration time using Aura. Integration time was defined as the time between initial start date of creating “orderables” to time of site testing. The number of EHR medical center analysts was also quantified. We used our prior P2P EHR deployments as a historical control. Results: Six bidirectional Tempus genomic integrations using Aura were performed from July 2022 to December 2022. All medical centers were expansive, multisite hospital systems. Mean number of integrated departments per hospital was 249 (range: 19-699). In 17 P2P integrations prior to Aura, mean integration time was 6.7m (range: 4m-18m). In contrast, mean integration time using Aura over the 6 sites was 1.5m (range: 0.75m-2m), which is a 78% improvement. No correlation was noted between the integration time and number of EHR analysts staffed or departments integrated. The two slowest integration sites (2m and 2.5m) had competing EHR deployment priorities. Conclusions: Using Aura, the speed of Tempus EHR integration was improved by 78% for bidirectional, discrete genomic data. Dedicated EHR integration resourcing reduced deployment times to as low as 3 weeks. This reduction in integration time may further the adoption of genomic EHR integrations and improve patient care by expediting time to appropriate, targeted treatments.
Electronic health record (EHR) and genomics-based machine learning (ML) to predict therapeutic effectiveness among patients with hormone-receptor positive (HR)+/HER2- advanced breast cancer (aBC).
Authors: Hathi Deep K. , Pisano Steven , Honarvar Hossein , Parikh Ravi Bharat , Das Rahul K.
Published: June 2023

e13574

Background: Patients (pts) with HR+/HER2- aBC may eventually become resistant to endocrine therapies and CDK4/6 inhibitors (CDK4/6i). ML algorithms on EHR linked with NGS data may enable more accurate predictions of therapeutic resistance and identify clinicogenomic (CG) risk factors. Methods: HR+/HER2- aBC pts treated between Jan 2015 – Dec 2022 with at least one NGS test report before line end were identified in ConcertAI’s Genome360 oncology database, consisting of molecular alterations abstracted from NGS reports linked with deeply curated US-based EHR. XGBoost (XGB) and Cox proportional hazards (CoxPH) models were trained to predict real-world progression-free survival (rwPFS) derived from curated tumor progression (TP), as assessed by the providers based on clinical observations, pathology results, and radiologic evidence. Data was split into 60:20:20 for training/validation/test. Pts in the 1st and 4th quartiles of predicted risk from XGB model were defined as low- and high-risk. Predictors were identified from Shapley Additive exPlanations (SHAP)-derived marginal hazard ratios (HR), derived in the 180 days prior to index date, and included demographics, labs, vitals, comorbidities, concomitant medications, procedures, tumor properties, and molecular alterations as features. Results: Among 624 pts (CDK4/6i treated = 519) with median age = 62.5 years and 10.5% black, 46.8% developed TP. The test cumulative dynamic ROC-AUC in the best-performing XGB model was 0.68. The precision at 180 days (%TP = 27) was 40%. The 180-day cumulative incidence of TP in low- and high-risk groups was 11.6% vs 37.7%. Higher line number, lower hemoglobin (HGB), higher alkaline phosphatase (ALP), presence of liver metastasis, and history of thoracic radiation were clinical risk factors. Genomic risk factors were FGF aberrations, alterations in TP53 and in genes within the MAPK, cell cycle, and ESR pathways, and co-alterations in the PIK3CA+TP53, and MAPK+ESR pathways. ALP-by-TP53 interaction was found. ALP level was a risk factor in TP53-wildtype (wt) but not in TP53-mutant (mut) pts. Conclusions: ML on EHR linked with NGS data enables identification of high-risk pts and multimodal predictors of CDK4/6i resistance among women with HR+/HER2- aBC, identifying both known and undescribed interactions between clinical and genomic risk factors as well as co-alteration risk factors. [Table: see text]