Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.
Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 380 - 394 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471458&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471458 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471458 184471458 184471458 10.1007/s10278-024-01132-8 184471458 ppf: 380 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer. aug: au: Zhou, Jie Xia, Yujia Xun, Xiaolei Yu, Zhangsheng affil: https://ror.org/0220qvk04 Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China sug: subj: Liver Neoplasms Drug Therapy Liver Neoplasms Diagnosis Liver Neoplasms Radiography Immunotherapy Deep Learning Detection Algorithms Tomography, X-Ray Computed Methods Clinical Assessment Tools Evaluation Treatment Outcomes Human Funding Source Male Female Middle Age Aged Aged, 80 and Over Prospective Studies Retrospective Design Descriptive Statistics Comparative Studies Data Analysis Software Paired T-Tests Wilcoxon Rank Sum Test ROC Curve Regression Multicenter Studies Sensitivity and Specificity Carcinoma, Hepatocellular Neoplasm Metastasis Artificial Intelligence Decision Making, Clinical Conceptual Framework Imaging, Three-Dimensional Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10−4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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