A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer
OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 pa...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748402/ https://www.ncbi.nlm.nih.gov/pubmed/36515714 http://dx.doi.org/10.1007/s00330-022-09337-7 |
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author | Zhu, Zhenchen Chen, Minjiang Hu, Ge Pan, Zhengsong Han, Wei Tan, Weixiong Zhou, Zhen Wang, Mengzhao Mao, Li Li, Xiuli Sui, Xin Song, Lan Xu, Yan Song, Wei Yu, Yizhou Jin, Zhengyu |
author_facet | Zhu, Zhenchen Chen, Minjiang Hu, Ge Pan, Zhengsong Han, Wei Tan, Weixiong Zhou, Zhen Wang, Mengzhao Mao, Li Li, Xiuli Sui, Xin Song, Lan Xu, Yan Song, Wei Yu, Yizhou Jin, Zhengyu |
author_sort | Zhu, Zhenchen |
collection | PubMed |
description | OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75–0.82) compared with the largest lesion approach (AUCs: 0.70–0.78) and the average sum approach (AUCs: 0.64–0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84–0.89], 0.75 [0.68–0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89–0.93], p = .042; validation: AUC 0.86 [0.80–0.91], p = .011; test: AUC 0.86 [0.76–0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40–2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09337-7. |
format | Online Article Text |
id | pubmed-9748402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-97484022022-12-14 A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer Zhu, Zhenchen Chen, Minjiang Hu, Ge Pan, Zhengsong Han, Wei Tan, Weixiong Zhou, Zhen Wang, Mengzhao Mao, Li Li, Xiuli Sui, Xin Song, Lan Xu, Yan Song, Wei Yu, Yizhou Jin, Zhengyu Eur Radiol Chest OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75–0.82) compared with the largest lesion approach (AUCs: 0.70–0.78) and the average sum approach (AUCs: 0.64–0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84–0.89], 0.75 [0.68–0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89–0.93], p = .042; validation: AUC 0.86 [0.80–0.91], p = .011; test: AUC 0.86 [0.76–0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40–2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09337-7. Springer Berlin Heidelberg 2022-12-14 2023 /pmc/articles/PMC9748402/ /pubmed/36515714 http://dx.doi.org/10.1007/s00330-022-09337-7 Text en © The Author(s), under exclusive licence to European Society of Radiology 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Chest Zhu, Zhenchen Chen, Minjiang Hu, Ge Pan, Zhengsong Han, Wei Tan, Weixiong Zhou, Zhen Wang, Mengzhao Mao, Li Li, Xiuli Sui, Xin Song, Lan Xu, Yan Song, Wei Yu, Yizhou Jin, Zhengyu A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title | A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title_full | A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title_fullStr | A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title_full_unstemmed | A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title_short | A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
title_sort | pre-treatment ct-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748402/ https://www.ncbi.nlm.nih.gov/pubmed/36515714 http://dx.doi.org/10.1007/s00330-022-09337-7 |
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