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radiomic features were then extracted and used to train a radiomics-based machine learning classifier.",{"name":70,"@type":61,"acceptedAnswer":71},"What performance was reported for the detection tool and the NE phenotype classifier?",{"text":72,"@type":64},"Lesion-based sensitivities were 66%–83% across the two datasets, and the NE phenotype classifier achieved an AUC of 0.73 and an F1 score of 0.88 in the testing cohort.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},372034,1790367393,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":116,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},962090895452,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","© Copyright 2026 Author(s) -[Available online at dirjournal.org](Available online at dirjournal.org). Content of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  \nORIGINAL ARTICLE  \nAutomated detection and characterization of small cell lung cancer liver metastasis on computed tomography  \n Sophia Ty 1  \n Fahmida Haque 1  \n Parth Desai2  \n Nobuyuki Takahashi3,4  Usamah Chaudhary 1,5  Peter L. Choyke 1  \n Anish Thomas4  \n Barış Türkbey 1  \n Stephanie A. Harmon 1  \n1National Cancer Institute, Artificial Intelligence Resource, Maryland, USA  \n2 Fox Chase Cancer Center at Temple University Hospital, Philadelphia, USA  \n3National Cancer Center Hospital East, Department of Medical Oncology, Kashiwa, Japan  \n4National Cancer Institute, Developmental Therapeutics Branch, Maryland, USA  \n5University of Texas Southwestern Medical Center, Texas, USA  \nHandling editor: Tuğba Akıncı D’Antonoli  \nCorresponding author: Barış Türkbey  \nE‑mail: [turkbeyi@mail.nih.gov](turkbeyi@mail.nih.gov)  \nReceived 28 February 2025; revision requested 24 March 2025; last revision received 14 July 2025; accepted 11 September 2025.  \nPURPOSE  \nSmall cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we present a computational approach to detect and characterize metastatic SCLC (mSCLC) liver lesions and their associated NE-related phenotype as a method to improve patient management.  \nMETHODS  \nThis study utilized computed tomography scans of patients with hepatic lesions from two data sources for segmentation and classification of liver disease: (1) a public dataset from patients of various cancer types (segmentation; n = 131) and (2) an institutional cohort of patients with SCLC (segmentation and classification; n = 86) . We developed deep learning segmentation algorithms and compared their performance for automatically detecting liver lesions, evaluating the results with and without the inclusion of the SCLC cohort. Following segmentation in the SCLC cohort, radiomic features were extracted from the detected lesions, and least absolute shrinkage and selection operator regression was utilized to select features from a training cohort (80/20 split) . Subsequently, we trained radiomics-based machine learning classifiersto stratify patients based on their NE tumor profile, defined as expression levels of a preselected gene set derived from bulk RNA sequencing or circulating free DNA chromatin immunoprecipitation sequencing.  \nRESULTS  \nOur liver lesion detection tool achieved lesion-based sensitivities of 66%–83% for the two datasets. In patients with mSCLC, the radiomics-based NE phenotype classifier distinguished patients as positive or negative for harboring NE-like liver metastasis phenotype with an area under the receiver operating characteristic curve of 0.73 and an F1 score of 0.88 in the testing cohort.  \nCONCLUSION  \nWe demonstrate the potential of utilizing artificial intelligence (AI)-based platforms as clinical decision support systems, which could help clinicians determine treatment options for patients with SCLC based on their associated molecular tumor profile.  \nCLINICAL SIGNIFICANCE  \nTargeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.  \nKEYWORDS  \nComputer vision, segmentation, neuroendocrine gene expression, radiomics, tumor classification, transcriptomics, molecular tumor profile  \nEpub: 06.10.2025  \nPublication date: 01.07.2026  \nDOI: 10.4274/dir.2025.253310  \nYou may cite this article as: Ty S, Haque F, Desai P, et al. Automated detection and characterization of small cell lung cancer liver metast","cbCaipd0E6cO9oon","https://ap.wps.com/l/cbCaipd0E6cO9oon","pdf",1964633,"English","# Purpose\n# Methods\n# Results\n# Conclusion\n# Clinical Significance\n# Keywords\n# Background and Main Points","[{\"question\":\"What is the purpose of the proposed AI approach for SCLC liver metastasis?\",\"answer\":\"It aims to automatically detect and characterize metastatic SCLC liver lesions on computed tomography and infer the associated NE-related phenotype to improve patient management.\"},{\"question\":\"How were liver lesions detected and classified in this study?\",\"answer\":\"Computed tomography scans were segmented using deep learning models trained on a public multi-cancer dataset and an institutional SCLC cohort; radiomic features were then extracted and used to train a radiomics-based machine learning classifier.\"},{\"question\":\"What performance was reported for the detection tool and the NE phenotype classifier?\",\"answer\":\"Lesion-based sensitivities were 66%–83% across the two datasets, and the NE phenotype classifier achieved an AUC of 0.73 and an F1 score of 0.88 in the testing cohort.\"}]","Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography | PDF",1790193509,23]