Fine-tuning pre-trained extractive QA models for clinical document parsing

Electronic health records (EHRs) contain a vast amount of high-dimensional multi-modal data that can accurately represent a patient's medical history. Unfortunately, most of this data is either unstructured or semi-structured, rendering it unsuitable for real-time and retrospective analyses. A remote patient monitoring (RPM) program for Heart Failure (HF) patients needs to have access to clinical markers like EF (Ejection Fraction) or LVEF (Left Ventricular Ejection Fraction) in order to ascertain eligibility and appropriateness for the program. This paper explains a system that can parse echocardiogram reports and verify EF values. This system helps identify eligible HF patients who can be enrolled in such a program. At the heart of this system is a pre-trained extractive QA transformer model that is fine-tuned on custom-labeled data. The methods used to prepare such a model for deployment are illustrated by running experiments on a public clinical dataset like MIMIC-IV-Note. The pipeline can be used to generalize solutions to similar problems in a low-resource setting. We found that the system saved over 1500 hours for our clinicians over 12 months by automating the task at scale

Co-Localization of Gastrointestinal Stromal Tumors (GIST) and Peritoneal Mesothelioma: A Case Series.

PURPOSE: Gastrointestinal stromal tumor (GIST) is associated with increased risk of additional cancers. In this study, synchronous GIST, and peritoneal mesothelioma (PM) were characterized to evaluate the relationship between these two cancers. METHODS: A retrospective chart review was conducted for patients diagnosed with both GIST and PM between July 2010 and June 2021. Patient demographics, past tumor history, intraoperative reports, cross-sectional imaging, peritoneal cancer index (PCI) scoring, somatic next-generation sequencing (NGS) analysis, and histology were reviewed. RESULTS: Of 137 patients who underwent primary GIST resection from July 2010 to June 2021, 8 (5.8%) were found to have synchronous PM, and 4 patients (50%) had additional cancers and/or benign tumors. Five (62.5%) were male, and the median age at GIST diagnosis was 57 years (range: 45-76). Seventy-five percent of GISTs originated from the stomach. Of the eight patients, one patient had synchronous malignant mesothelioma (MM), and the remaining had well-differentiated papillary mesothelioma (WDPM), which were primarily located in the region of the primary GIST (89%). The median PCI score was 2 in the WDPM patients. NGS of GIST revealed oncogenic KIT exon 11 (62.5%), PDGFRA D842V (25%), or SDH (12.5%) mutations, while NGS of the MM revealed BAP1 and PBRM1 alterations. CONCLUSIONS: One in 17 GIST patients undergoing resection in this series have PM, which is significantly higher than expected if these two diseases were considered as independent events. Our results indicate that synchronous co-occurrence of GIST and PM is an underrecognized finding, suggesting a possible relationship that deserves further investigation

Location of gastrointestinal stromal tumor (GIST) in the stomach predicts tumor mutation profile and drug sensitivity

Purpose: Gastrointestinal stromal tumors (GIST) commonly arise in different regions of the stomach and are driven by various mutations (most often in KIT, PDGFRA, and SDHx). We hypothesized that the anatomic location of gastric GIST is associated with unique genomic profiles and distinct driver mutations. Experimental Design: We compared KIT versus non-KIT status with tumor location within the National Cancer Database (NCDB) for 2,418 patients with primary gastric GIST. Additionally, we compiled an international cohort (TransAtlantic GIST Collaborative, TAGC) of 236 patients and reviewed sequencing results, cross-sectional imaging, and operative reports. Subgroup analyses were performed for tumors located proximally versus distally. Risk factors for KIT versus non-KIT tumors were identified using multivariate regression analysis. A random forest machine learning model was then developed to determine feature importance. Results: Within the NCDB cohort, non-KIT mutants dominated distal tumor locations (P < 0.03). Proximal GIST were almost exclusively KIT mutant (96%) in the TAGC cohort, whereas 100% of PDGFRA and SDH-mutant GIST occurred in the distal stomach. On multivariate regression analysis, tumor location was associated with KIT versus non-KIT mutations. Using random forest machine learning analysis, stomach location was the most important feature for predicting mutation status. Conclusions: We provide the first evidence that the mutational landscape of gastric GIST is related to tumor location. Proximal gastric GIST are overwhelmingly KIT mutant, irrespective of morphology or age, whereas distal tumors display non-KIT genomic diversity. Anatomic location of gastric GIST may therefore provide immediate guidance for clinical treatment decisions and selective confirmatory genomic testing when resources are limited