bluuebunny/crossref_metadata_2025 huggingface dataset:
Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: A multicentre study.
Authors: Hu Yalan , Cai Zhenhai , AiErken NiJiati , Liu Yueqi , Shao Nan , Shan Zhen , Zhang Xiaoling , Lin Ying
Published: June 2023

e13542

Background: The study aimed to develop and optimize a radiomics model with features extracted from breast intra- and peri-tumoral region in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4, thereby obviating unnecessary biopsies. Methods: We retrospectively analyzed data of women with BI-RADS 4 or 5 lesions from three hospitals. The regions of interest (ROIs) of intra-tumoral regions were delineated manually on each slice of image. The peri-tumoral regions were then obtained by equidistant 3-dimensinal dilation of the tumor border with 1, 2 and 3mm, respectively. Seven classification models were built with features extracted from the intra- and peri-tumoral regions, including the Intra, Peri1mm, Peri2mm, Peri3mm, Intra+peri1mm, Intra+peri2mm and Intra+peri3mm. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC) and compared by DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern. To assess the rate of avoidable biopsies, three exploratory cutoff values on the ROC curve were examined. Results: The Comb2 model, built with features from peri-tumoral 2mm and intra-tumoral region, demonstrated the best performance with AUCs of 0·901 and 0·863 in the development and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement lesions. At the three exploratory cutoff values on the ROC curve, the model identified 11·2% (sensitivity of C1≥90%), 49·5% (sensitivity of C2≥95%) and 63·3% (sensitivity of C3≥98%) of the benign lesions in the development cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions. Conclusions: An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.
Toward automating RECIST 1.1: Improving AI new lesion detection with longitudinal image data.
Authors: Gong Amanda , Daly Morgan , Melendez-Corres Gabriel , Teng Pang Yu , Hukkeri Tanmay Sanjay , Muhammad Wahi-Anwar , Ruchalski Kathleen , Goldin Jonathan , Brown Matthew S.
Published: June 2023

e13545

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

e13548

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

e13549

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

e13552

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

e13553

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

e13558

Background: While mammography is currently the standard of care for breast cancer screening, dense breast tissue can significantly degrade results. Alternatively, infrared spectroscopy analysis of breath offers a highly sensitive method for identifying exhaled volatile organic compounds (VOCs), which may circumvent issues with breast density. Methods: Alveolar breath samples were collected onto Tenax TA sorbent tubes using a SohnoXB™ breath sampler. Using four desorb temperatures (75, 150, 225 and 300°C), absorption spectra were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a technique for measuring absorption coefficients due to VOCs in exhaled breath. Missing values in the absorption spectra were backfilled using interpolation, and the spectrum was min-max normalized and quadratic detrended. First and second derivatives of the preprocessed absorption spectra were used as features for a support vector machine machine-learning model. Features were ranked based on minimum redundancy maximum relevance (mRMR). The top 20 ranked features were selected to limit the potential for overfitting and then optimized. Model performance was validated using non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV to provide optimistic and pessimistic results, respectively. Results: Absorption spectra from 111 participants (71 positive, 40 control) were used. Of the positive subjects, 30 had low- and 31 had high-density breast tissue (measures missing for 10). Model performance is outlined. A subgroup analysis compared model performance for subjects with low- and high-density breast tissue for both non-nested and nested LOOCV models. Breast density data was not captured for control subjects thus they were not included in the subgroup analysis. Fisher’s exact test was performed to assess for significant difference between model performance for those with low- vs high-density breast tissue, resulting in a p-value of 1.00. Conclusions: Our results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer that is independent of breast density. Breath analysis may therefore become a new alternative or corroborative to mammography to support clinical decision-making. [Table: see text]
HER2 in situ hybridization testing in breast cancer: Applying algorithm-assisted assessment to reduce interobserver variability in difficult cases.
Authors: Gough Madeline , Liu Cheng , Srinivasan Bhuvana , Wilkinson Lisa , Dunk Louisa , Yang Yuanhao , Tuffaha Haitham , Kryza Thomas , Hooper John D. , Lakhani Sunil R. , Snell Cameron Edward
Published: June 2023

e13561

Background: Accurate assessment of HER2 expression by HER2 immunohistochemistry and in-situ hybridization (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines defined 5 subgroups based on HER2 expression and copy number. Few reports have investigated interobserver variability when applying the revised guidelines. Digital algorithms may have a role in assisting pathologists in assessing cases with equivocal and uncommon patterns of HER2 amplification. This study was designed to evaluate interobserver variability in the gold standard manual assessment of HER2 ISH, particularly in less common HER2 ISH groups. Subsequently, we evaluated whether a new digital image analysis algorithm (Roche uPath HER2 Dual ISH image analysis) could reduce interobserver variability and potentially improve patient selection for targeted anti-HER2 therapy. Methods: Of 2982 breast cancer cases, we derived an enriched cohort of 99 patient cases with HER2 IHC 2+ or 3+ and selective ISH results (groups 2-4, evidence of HER2 heterogeneity, borderline negative group 5 and borderline positive group 1). As per guidelines, a minimum of 20 tumour cells per slide were manually and independently counted by 4 anatomical pathologists. After a washout period and second round of de-identification, the slides were scanned (DP200; Roche) and re-scored using the digital algorithm. Results: Pathologist reporting of patient HER2 status (HER2 positive vs. HER2 negative) using manual microscopy demonstrated significant interobserver variability, with a Fleiss’s kappa value of 0.471 (95% CI: 0.378-0.564; P= 1.24E-21) indicating fair-moderate agreement. Interobserver variability in manual scoring of HER2 ISH at the level of HER2 group designation (groups 1-5) showed poor-moderate reliability between pathologists, with an intraclass correlation coefficient (ICC) of 0.526 (95% CI: 0.407-0.641; P= 9.28E-21). Repeat analysis of the cohort using algorithm-assisted quantification resulted in moderate-substantial concordance in HER2 patient status reporting, with a Fleiss’s kappa value of 0.666 (95% CI: 0.572-0.759; P= 1.70E-40). Algorithm-assisted quantification resulted in moderate-good agreement of HER2 group designation, with an ICC of 0.763 (95% CI: 0.683-0.832; P= 8.76E-48). In sub-group analysis, use of the algorithm improved concordance particularly in groups 2,4 and 5. Time to report cases was also significantly reduced with the use of the algorithm. Conclusions: Our study demonstrates the potential to improve the concordance of HER2 amplification status reporting in less common HER2 subgroups using a digital algorithm and potentially improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.
Machine learning prediction of patient-reported outcomes associated with systemic therapy from medical records.
Authors: Lin Frank Po-Yen , Joshua Anthony M. , Epstein Richard J.
Published: June 2023

e13564

Background: Measures of patient-reported outcome (PRO) are important metrics in cancer care. However, PROs are poorly captured in routine practice, and data collection may incur undue burden on patients. It remains unclear if electronic medical records (EMR) contain sufficient information to dynamically monitor PROs in patients undergoing intensive systemic therapy. Methods: Over a 6 week period, consecutive patients attending an outpatient chemotherapy unit completed two questionnaires (EORTC QLQ-C30 and NCI PRO-CTCAE) at each visit. The corresponding EMR, authored by oncologists and allied health providers, were extracted up to 4 weeks prior to the questionnaire date. Random forest algorithm was used to predict each dichotomized PRO class using bag-of- n-grams extracted from the text. The classification performance was assessed by 8-fold cross-validation, using the area under receiver operating characteristic curve (AUC) as the metric. This study is part of the ePERSIST (electronic phenotyping, event retrieval, and case summarisation in solid tumours) project, exploring methods for extracting structured variables from EMR to support oncology research. Results: 115 patients (157 visits to the chemotherapy unit) completed up to 6 sets of questionnaires each. Overall, the median quality-of-life (QOL) score of QLQ-C30, QL2, was 67 (IQR 50 to 83). Machine learning predicted low QL2 score with mean AUC of 0.710 (95% CI 0.630 to 0.791). At the Bonferroni threshold of 0.00025, machine learning significantly predicted several components of QLQ-C30: PF2 (mean AUC 0.722, 0.633 to 0.812), AP (0.725, 0.630 to 0.819), CO (0.686, 0.607 to 0.764), FI (0.677, 0.598 to 0.755), PA (0.696, 0.603 to 0.787), FA (0.686, 0.584 to 0.787), and RF2 (0.717, 0.595 to 0.839). Conversely, EMR could only be used to significantly predict 4 of 13 PRO-CTCAE categories: cutaneous (0.745, 0.659 to 0.832), oral (0.734, 0.630 to 0.838), cardiovascular (0.675, 0.593 to 0.758), and gastrointestinal (GI, 0.742, 0.625 to 0.860), or 15 of 79 (19%) of PRO-CTCAE items related to adverse events including cutaneous (radiation burns, hypohidrosis, rash), oral (anorexia, dysphagia, dysgeusia), and GI symptoms (constipation, bloating), and others (anxiety, hoarseness, pain, erectile dysfunction). An exploratory analysis revealed that specific keywords linked to the PRO items were present in only one (11%) QLQ-C30 and 4 (5%) PRO-CTCAE items. Conclusions: The use of machine learning on clinical text predicts QOL and certain patient-reported adverse events with moderate accuracy in individuals undergoing intensive systemic treatment, suggesting its potential utility in syndromic monitoring where PRO is not formally assessed. Given many items cannot be accurately extracted, however, obtaining information directly from patients through questionnaires may still be required because of limited documentation by clinicians.
Evaluating clinical trial inclusion/exclusion criteria from claims using generative artificial intelligence.
Authors: Mueller Judith , Parikh Ravi Bharat , Noble Andrew
Published: June 2023

e13566

Background: Increasing enrollment in oncology clinical trials (CTs) requires identification of trial-eligible patients from routine health data. However, identification of CT-eligible patients is limited by inadequate access to EHR and its relevant clinical features to evaluate inclusion/exclusion (I/E) criteria in large populations. Artificial Intelligence (AI) applied to large-scale administrative claims may accelerate pre-screening of large patient cohorts against common I/E criteria related to stage and performance status (PS). Methods: This retrospective cohort study included 92,895 adult patients from ConcertAI’s real-world data: linked claims and deeply curated de-identified EHRs sourced from data-sharing agreements with 900+ community oncology clinics in the US. Patients had solid malignancies and an oncology encounter between January 6, 2014, and August 12, 2022. Patients with ≥1 claim record within one week of a PS entry and ≥25 claims within three years of a clinical encounter were included. We trained a Generative AI Transformer Model to predict good PS (ECOG 0-1 or Karnofsky 70-100) and stage IV disease at baseline, both of which are common inclusion criteria for therapeutic CTs in advanced malignancies. Model features included 7,745 timestamped claim codes and 8 time-independent demographic and socioeconomic variables. A state-of-the-art Generative AI Pre-trained Transformer Model was fine-tuned via compute-efficient transfer-learning to obtain predictive models for both PS and stage at baseline. The train/validation/test patient split was 95%/2%/3% with 3,039 patients in the PS test set and 2,718 in the stage IV test set. A comparator model was built to predict stage IV at baseline if a C77-9 ICD-100CM secondary malignancy code or cross-walked ICD-9-CM was in the claim record. Results: Among 92,895 patients, 48,331 (52%) were women, 63,620 (68%) were ≥65 years old, 54,495 (59%) were White, and 6,603 (7%) were Black. 38,229 (41%) were confirmed stage IV at baseline and 64,177 (69%) had good PS. In the test set predicting good PS, area under the receiver operating characteristic curve (AUROC) was 0.8 and under the precision-recall curve (AUPRC) was 0.89 for the Generative AI model. This corresponded to an Equal Error Rate (EER) operating point with good precision and recall (both 0.82). Performance metrics for the stage IV endpoint were also acceptable (AUROC 0.84, AUPRC 0.87, EER operating point 0.77). Comparator stage IV precision was 0.73 and recall was 0.47. Conclusions: Generative AI can leverage claims-only data to evaluate common pre-screening I/E criteria in large cohorts and outperforms models based on limited diagnosis codes. Rather than building one model per criterion per indication, Generative AI enables a systematic and reliable solution that can be readily scaled in future large-scale CT screening.
Individualized treatment estimates to inform on personalized cancer care decisions for treatment selection and treatment management.
Authors: Walsh David , Srivatsa Sumana , George Renee , Ellis Ben , Aljehani Mayada , Gross Mitchell E. , Basho Reva K. , Matasci Naim , Swisher Christine
Published: June 2023

e13571

Background: Clinical trials report average effects of treatments at a population level. However, clinicians must balance treatment benefits against risks to personalize therapy. Here, we present a statistical framework which incorporates real-world data to estimate efficacy and symptom burden for an individual patient. We then assess the potential impact of continuing immunotherapy beyond progression in patients with metastatic solid malignancies on immune-related adverse events, progression, and survival. Methods: We corrected for sampling biases with inverse propensity score weighting (IPTW), and used the IPTW-transformed data to train predictive models of patient outcomes under each option. The models utilize a peer-based similarity based on 222 routinely-collected patient characteristics, such as cancer and treatment specific factors, laboratory tests, demographics, vitals, stage, comorbidities and symptoms, which are extracted from the electronic health record or natural language processing of clinician narratives, and Social Determinants of Health from Census data. We developed our methods on “Dataset A”: 5,919 breast and genitourinary (GU) cancer patients at a tertiary cancer center. We computed individualized treatment effect (ITE) estimates on “Dataset B”: 5,486 breast, GU, and thoracic cancer patients who received immunotherapy. Results: Our approach eliminated biases in Dataset A: the IPTW-transformation reduced the between-group standardized mean difference across identified confounders from an average of 0.225 to an average of 0.022. The similarity model exhibited strong discrimination (ROC-AUC > 0.75), and identified associations between patient characteristics and outcomes that matched clinicians’ expectations. Validations are ongoing to assess the ITE estimates on a held-out subset of Dataset B. These include subgroup analyses for metastatic triple-negative breast cancer and non-small cell lung cancer. Conclusions: We introduce a method to estimate the efficacy and toxicity of interventions, actions, or treatment decisions, which replicates the statistical robustness of a clinical trial while obtaining individualized insights. This framework provides causal estimates of symptom burden and risks of progression and survival to inform clinical decisions, including continuation of immunotherapy beyond progression. After IPTW, confounders in Dataset A were balanced between the treatment groups, with weighted standardized mean differences <0.1. [Table: see text]
Augmented curation of disease diagnoses and medications for patients with hepatocellular carcinoma.
Authors: Ip Wui , Pawlowski Colin , Mathew Vineet , Choudhary Mayank , Niesen Michiel , Anand Akash , Mao Allen , Peddinti Ananth , Kancharla Chetan , Kunz Isaac , Bonner Joseph D. , Boyle Matthew , Jinadu Olamilekan , Pozhitkov Alex , Xia Xiaoyu , Sarmiento Raul , Berger Stacy , Kirkpatrick Tarrah , Soundararajan Venky , Courdy Samir
Published: June 2023

e13573

Background: Unstructured EHR data contains nuanced rich insights for Real World Evidence (RWE) studies, however harnessing this unstructured data at scale can be challenging. Here, we evaluated two Natural Language Processing-based curation (“augmented curation”) models to identify medical conditions and medications from clinical notes for patients with hepatocellular carcinoma at City of Hope National Medical Center. Methods: We deployed two augmented curation models originally trained on de-identified EHR data from the nference nSights platform, a federated data platform comprising clinical data from several Academic Medical Centers, on clinical notes found in the City of Hope POSEIDON platform, an oncology insights engine. We assessed their performances using manual chart review as reference. We then calculated an Enrichment Factor (EF) score for augmented curation model extraction and its 95% confidence intervals. The EF score was defined as the number of patients captured by either augmented curation or structured data divided by the number of patients captured by structured data alone. Results: The augmented curation models captured conditions and medications from clinical notes with F1 scores of 0.93 and 0.95, respectively. Compared to structured EHR data alone, the disease diagnosis model captured significantly more individuals with signs and symptoms such as vomiting (EF: 22.8, 95% CI: [15.2, 41.1]), weight loss (EF: 5.0, 95% CI: [4.1, 6.6]), nausea (EF: 4.3, 95% CI: [3.1, 6.9]), and edema (EF: 3.1, 95% CI: [2.7, 3.7]). Similarly, the medication use augmented curation model captured significantly more individuals with medications such as interferon, aspirin, and pembrolizumab. From a Cox proportional hazards model analysis, we found that a survival model using augmented curation-based features and structured data achieved a model concordance of 0.745 (95% CI: [0.741, 0.748]), compared to 0.722 (95% CI: [0.717, 0.726]) for the model based on structured data alone. In addition, the augmented curation-based survival model identified jaundice (HR: 2.0, 95% CI: [1.5, 2.7]) as a significant risk factor for mortality, which was not picked up by the structured data-based survival model. Conclusions: Overall, this study shows that augmented curation models can be used to accurately capture comorbidities and medications from unstructured clinical notes, and these extracted covariates are correlated with clinically meaningful endpoints such as survival. We recommend using augmented curation as a standard insight generation approach in RWE study protocols.
Performance analysis of four machine learning algorithms for the accurate prediction of metastatic disease in cutaneous squamous cell carcinoma.
Authors: Andrew Tom William , Bolnykh Iakov , Bowes Amy Louise , Fernando Suhari Arahliya Serendibsha Grace , Nair Ashvati , Martin Sabrina Noor Pia , Maan Balraj , Sloan Philip , Lovat Penny , Rose Aidan
Published: June 2023

e13579

Background: Cutaneous squamous cell carcinoma (cSCC) are the most common form of metastasising skin cancer. Whilst rates of metastatic cSCC are low, they account for a significant proportion of skin cancer related morbidity and mortality, particularly within elderly cohorts, which poses a significant burden to healthcare services. Established cSCC tumour staging systems perform poorly at predicting metastatic risk. Additionally, we lack clinically validated prognostic biomarkers – highlighting the unmet need for novel risk stratification tools to guide clinical practice and improve outcomes for patients with advanced disease. We aimed to train four recognised machine learning (ML) algorithms on a large clinic-pathological dataset of primary cSCC, with the objective of optimising an ML strategy and developing a reliable and clinically useful risk stratification tool capable of accurately predicting metastatic events following primary cSCC. Methods: A dataset of primary cSCC registrations was derived from the Northern Cancer Registry, UK. This identified 7003 histologically confirmed primary cSCC registered between 2010–2020; providing a minimum of 2 years clinical follow-up. We conducted a retrospective analysis of standardised pathology datasets, recording clinical-pathological features. Primary outcome measure was regional and/or distant metastasis. Four machine learning algorithms, were trained based on these features, including: a Logistic Regression Trainer, a Decision Tree Classifier, a Random Forest Classifier and a fully connected artificial neural network (ANN). The algorithms were optimised on training data using five-fold cross validation. Subgroup analysis was performed using mean Shapley additive explanations (SHAP). Results: Accuracy scoring identified the ANN as the optimal predictor of metastasis (0.94), followed by: Logistic Regression Trainer (0.82), Random Forest Classifier (0.80), and Decision Tree Classifier (0.71). Preliminary subgroup analysis identified immunosuppression as most sensitive risk factor for developing metastatic disease (SHAP = 0.122). Conclusions: Significant heterogeneity in current morbidity and mortality data has limited the capacity of traditional statistical models and tumour staging systems to identify very high-risk cSSC. Our findings demonstrate that ML algorithms can accurately predict metastatic events in cSSC populations. Further development of a model user-interface is necessary to support the development of a useful risk stratification tool to guide clinical practice.
Association of presenting symptoms with COVID-19 disease severity and outcomes among hospitalized patients with cancer and COVID-19.
Authors: Ali Noman , Kheder Ed , Dickson Kodwo , Rubio David , Koom-Dadzie Kwame , Edelkamp Paul , Lin Heather Y. , Muthu Mayoora , Mohammed Alyssa , Leung Cerena , Odaro Orhue , Sibille Michelle , George Marina C. , Halm Josiah , Manzano Joanna-Grace Mayo ,   UT MD Anderson Hospitalist
Published: June 2023

e13581

Background: COVID-19 has led to several waves of outbreaks over the last 2 years. Cancer patients are among the most vulnerable and have high morbidity and mortality rates. We aimed to determine the association between presenting symptoms and symptom burden and COVID hospitalization outcomes among cancer patients. Methods: We conducted a retrospective cohort study on all adult hospitalized cancer patients during their first admission at our cancer center from March 2020 to May 2021. We reviewed medical charts to identify reported symptoms. We used the Foundry data platform to compile and analyze data, including patient and hospitalization characteristics and outcomes. We used the chi-square test to test differences in categorical variables. Results: Our cohort included 595 patients, 272 with hematologic malignancies (45.7%) and 323 with solid tumor malignancies (54.3%). 52.1% were male, with an age range of 18 to 91 years (median age: 62). 64.2% self-identified as Caucasian, 16.1% as other, 14.3% as African American, and 3.5% as Asian. After univariate analysis, we found that female patients had higher rates of abdominal pain (21.4% vs. 14.5%, p=0.03), GI symptoms (36% vs. 27.4%, p=0.03), and sore throat (14.4% vs. 8.4%, p=0.02) than male patients. Smoking status or Charlson comorbidity score were not associated with any presenting symptoms. Solid tumor patients had higher rates of abdominal pain (26.6% vs. 7.4%, p <0.01) than patients with hematologic malignancies. Patients with hematologic malignancies had higher rates of fever (59.2% vs. 42.7%, p<0.01), cough (66.2% vs. 50.8%, p<0.01), and shortness of breath (53% vs. 41.8%, p<0.01) than solid tumor patients. Solid tumor patients more frequently reported more than 3 symptoms (52% vs. 39.2%, p<0.01) than patients with hematologic malignancies. In terms of COVID outcomes, patients with cough (34.4% vs. 12%, p<0.01), shortness of breath (38.5% vs. 12.9%, p <0.01), and fever (29.1% vs. 20.6%, p=0.02) were more likely to have severe COVID-19. Patients with more than 3 symptoms were more likely to have severe COVID-19 (31.7% vs. 21.3% vs. 7.8%, p< 0.01) than those with 1-3 symptoms or no symptoms. Patients with cough (15.1% vs. 6.4%, p<0.01) and shortness of breath (15.5% vs. 7.9%, p< 0.01) had higher inpatient mortality. Patients with chills (5.8% vs. 12.9%, p< value 0.03) and headache (4.4% vs. 12.7%, p=0.02) had lower inpatient mortality. Conclusions: Our study showed that certain presenting COVID-19 symptoms are associated with patient and clinical characteristics, severity of disease, and inpatient mortality among adult hospitalized cancer patients.
Impact of life-sustaining therapies on critically ill patients with cancer.
Authors: Shah Kanan , Matos Joao , Struja Tristan , Svoboda Josef , Celi Leo , Sauer Christopher Martin
Published: June 2023

e13590

Background: Advances in diagnosis and treatment have led to large reductions in mortality rates for patients with cancer, resulting in a steady increase in patients admitted to intensive care units (ICUs). However, there is conflicting evidence supporting the benefit of common life-sustaining therapies for critically ill patients, within this cohort. We examined differences in treatment effects of life-sustaining therapies between critically ill septic patients with and without cancer. Methods: Adults aged 18+ with a principal cancer diagnosis from 2008 - 2019, admitted to the ICU for sepsis, were extracted from publicly accessible ICU databases: Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Logistic regression estimated the likelihood of receiving mechanical ventilation (MV), renal replacement therapy (RRT) or vasopressors (VP) during ICU admission. Additionally targeted maximum likelihood estimation (TMLE) models estimated average treatment effects of MV, VP, and RRT on mortality. As TMLE uses machine learning models, it accommodates large numbers of covariates with complex, non-linear relationships. The method outputs Average treatment effect (ATE), an odds ratio representing the mean difference in outcomes in a hypothetical world in which all patients received treatment, compared to a hypothetical world in which no patients received treatment. Models were adjusted for age, sex, ethnicity, Charlson Comorbidity Index, SOFA score, hypertension, heart failure, asthma, COPD, CKD, and code status at admission. Results: A total of 7,120 adults met inclusion criteria. Septic patients with cancer did not have a significantly different likelihood of receiving MV (aOR [95%CI], 0.94 [0.6 - 1.46]), RRT (0.79, [0.33 - 1.93]), or VP (1.09, [0.74, 1.9]) than septic patients without cancer. Among patients with low SOFA scores, those with cancer were more likely than those without cancer to benefit from RRT. Otherwise, there was no statistically significant difference in mortality with the use of MV, RRT, and VP between patients with and without cancer (p>0.05). Conclusions: To our knowledge this is the first study to utilize contemporary, nationwide critical care data to establish a causal relationship between mortality and the use of common life-sustaining therapies for patients with cancer. Our study highlights the tremendous advances in cancer treatment over the last decade, leading to similar effects of common critical care interventions on mortality, regardless of cancer diagnosis. [Table: see text]
Sequential learning for pan-tumor detection of metastatic disease progression.
Authors: Green Foad H. , Rioth Matthew J. , Loving Joshua
Published: June 2023

e13591

Background: Detection of progression of patients with early stage cancer is a challenge for real world data from electronic health records (EHR). Temporal patterns common to patients’ data can detect patients with metastatic progression. A deep learning approach on longitudinal patient records of cancer lab testing, visit diagnosis codes, and cancer treatments was used to identify metastatic status. Methods: ICD-10-CM diagnosis codes, clinical lab values specific to cancer testing, and antineoplastic treatments were evaluated from longitudinal data of 128,614 patients who did not progress and 5,884 who did develop metastasis, across 47 tumor types in the Syapse Learning Health Network. Patients metastatic at diagnosis were excluded. Data were included beginning at the time of primary cancer diagnosis and ended for all surviving patients at an established administrative cutoff date. In the metastatic group, visits after the progression event were included but censored if they contained diagnosis codes specifically for secondary malignant neoplasms. A binary classification was developed using a multi-headed attention transformer in order to determine metastatic status. Patient history was sequentially aggregated to visit-level embeddings across their longitudinal record. Categorical layers were used for antineoplastics and diagnosis codes. Linear layers were used to embed lab values, as well as for visit intervals from primary cancer date and administrative cutoff date. All embeddings were used as feature inputs for the classifier. Pre-training random sampling of the non-metastatic population was applied to establish equally-weighted labels in a 80:10:10 split for training, validation, and testing sets; to account for the smaller population of patients who progressed to metastatic status. Results: This classification approach achieved 0.86 PR-AUC, 0.79 ROC-AUC, and 0.75 F1. This performance is comparable to published models trained for single tumor cohort prediction. With our censoring constraints, these model results are robust in the absence of routine signals of metastasis in the EHR, such as staging reports and diagnosis coding for distant metastases. Conclusions: This method generalizes, across multiple cancer types, an accurate classification of metastatic progression from patient visit history. This work is immediately useful for real world evidence data analysis complementing patients with metastases already captured in hospital registries. Using sequential deep learning with EHR data classes, this approach may be used to forecast metastatic progression for early intervention.