Clinically Explainable Prediction of Immunotherapy Response Integrating Radiomics and Clinico-Pathological Information in Non-Small Cell Lung Cancer †.
Simple Summary: Only 20% patients with non-small cell lung cancer respond to immunotherapy alone and 40% to immunotherapy in combination with chemotherapy. The PD-L1 value cutoff from immunohistochemistry that is used to select patients who would respond to immunotherapy lacks accuracy. A combinatio...
| Publicado en: | Cancers Vol. 17; no. 16; pp. 2679 - 2700 |
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| Autores principales: | , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Aug2025
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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=187558568&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187558568 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Aug2025 vid: 17 iid: 16 pid: 97109 pub: MDPI artinfo: ui: 187558568 187558568 187558568 10.3390/cancers17162679 187558568 ppf: 2679 ppct: 21 formats: tig: atl: Clinically Explainable Prediction of Immunotherapy Response Integrating Radiomics and Clinico-Pathological Information in Non-Small Cell Lung Cancer †. aug: au: Mitra, Jhimli Ghose, Soumya Thawani, Rajat affil: GE HealthCare, Niskayuna, NY 12309, USA sug: subj: Carcinoma, Non-Small-Cell Lung Pathology Carcinoma, Non-Small-Cell Lung Drug Therapy Immunotherapy Radiomics Machine Learning Prediction Models Natural Language Processing Random Forest Treatment Outcomes Human Male Female Adult Middle Age Aged Aged, 80 and Over Cancer Patients Retrospective Design Comparative Studies Descriptive Statistics Prospective Studies Tumor Markers, Biological Health Care Delivery, Integrated Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Simple Summary: Only 20% patients with non-small cell lung cancer respond to immunotherapy alone and 40% to immunotherapy in combination with chemotherapy. The PD-L1 value cutoff from immunohistochemistry that is used to select patients who would respond to immunotherapy lacks accuracy. A combination of other clinical biomarkers and radiomic biomarkers from CT should be analyzed for the selection of patients who would benefit from immunotherapy. The aim of our retrospective study was to develop a machine learning model that predicted treatment response from multimodal data (clinical, peritumoral and tumoral radiomics features). This combination of features outperfomed using radiomics or clinical variables alone. A large language model was further used to explain the response predictions in natural-language using the biomarkers that are comprehensible to clinicians. Background/Objectives: Immunotherapy is a viable therapeutic approach for non-small cell lung cancer (NSCLC). Despite the significant survival benefit of immune checkpoint inhibitors PD-1/PD-L1, on average; the objective response rate is around 20% as monotherapy and around 50% in combination with chemotherapy. While PD-L1 IHC is used as a predictive biomarker, its accuracy is subpar. Methods: In this work, we develop a machine learning (ML) method to predict response to immunotherapy in NSCLC from multimodal clinicopathological biomarkers, tumor and peritumoral radiomic biomarkers from CT images. We further learn a graph structure to understand the associations between biomarkers and treatment response. The graph is then used to create sentences with clinical hypotheses that are finally used in a Large Language Model (LLM) that explains the treatment response predicated on the biomarkers that are comprehensible to clinicians. From a retrospective study, a training dataset of NSCLC with n = 248 tumors from 140 subjects was used for feature selection, ML model training, learning the graph structure, and fine-tuning LLM. Results: An AUC = 0.83 was achieved for prediction of treatment response on a separate test dataset of n = 84 tumors from 47 subjects. Conclusions: Our study therefore not only improves the prediction of immunotherapy response in patients with NSCLC from multimodal data but also assists the clinicians in making clinically interpretable predictions by providing language-based explanations. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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