Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Peter L Choyke, Anish Thomas, Barış Türkbey, Stephanie A Harmon
{"title":"计算机断层扫描对小细胞肺癌肝转移的自动检测与表征。","authors":"Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Peter L Choyke, Anish Thomas, Barış Türkbey, Stephanie A Harmon","doi":"10.4274/dir.2025.253310","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Small 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.</p><p><strong>Methods: </strong>This 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 classifiers to 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.</p><p><strong>Results: </strong>Our 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.</p><p><strong>Conclusion: </strong>We 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.</p><p><strong>Clinical significance: </strong>Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.\",\"authors\":\"Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Peter L Choyke, Anish Thomas, Barış Türkbey, Stephanie A Harmon\",\"doi\":\"10.4274/dir.2025.253310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Small 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.</p><p><strong>Methods: </strong>This 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 classifiers to 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.</p><p><strong>Results: </strong>Our 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.</p><p><strong>Conclusion: </strong>We 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.</p><p><strong>Clinical significance: </strong>Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.</p>\",\"PeriodicalId\":11341,\"journal\":{\"name\":\"Diagnostic and interventional radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnostic and interventional radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.4274/dir.2025.253310\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic and interventional radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4274/dir.2025.253310","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.
Purpose: Small 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.
Methods: This 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 classifiers to 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.
Results: Our 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.
Conclusion: We 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.
Clinical significance: Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.
期刊介绍:
Diagnostic and Interventional Radiology (Diagn Interv Radiol) is the open access, online-only official publication of Turkish Society of Radiology. It is published bimonthly and the journal’s publication language is English.
The journal is a medium for original articles, reviews, pictorial essays, technical notes related to all fields of diagnostic and interventional radiology.