Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli
{"title":"非小细胞肺癌术前CT放射学分析和液体活检：一种探索性经验。","authors":"Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli","doi":"10.1111/1759-7714.70115","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Nonsmall cell lung cancer (NSCLC) remains a significant global health burden, necessitating advancements in diagnostic and prognostic strategies. Liquid biopsy and radiomics offer promising avenues for enhancing preoperative assessment and treatment planning in NSCLC.</p><p><strong>Methods: </strong>This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)-guided biopsy and liquid biopsy. Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations.</p><p><strong>Results: </strong>Among 57 patients with available data, associations between radiomic features and gene pairs mutation obtained from liquid biopsy exhibited moderate accuracy (approximately 0.60), with texture features demonstrating higher importance. However, when predicting the combined mutation status of gene pairs (e.g., EGFR and ROS1), the classification task involved three classes and yielded substantially lower accuracy (approximately 0.30), likely due to class imbalance and increased complexity.</p><p><strong>Discussion: </strong>Our findings demonstrate a moderate association between radiomic features and single gene mutations detected through liquid biopsy in NSCLC patients, with classification accuracies reaching approximately 0.60. In contrast, classification performance significantly declined (to ~0.30) when gene mutation pairs were used as targets, likely due to increased complexity and class imbalance. Notably, second-order texture features showed the highest importance in the models. These preliminary results suggest that radiomics may capture aspects of tumor biology reflected in liquid biopsy, warranting further validation in larger, well-balanced cohorts.</p><p><strong>Conclusion: </strong>The integration of liquid biopsy and radiomics holds promise for enhancing preoperative assessment and personalized treatment strategies in NSCLC. Further research on larger cohorts is warranted to validate the findings and translate them into clinical practice.</p><p><strong>Trial registration: </strong>University of Campania Trial Board UC20201112-24997.</p>","PeriodicalId":23338,"journal":{"name":"Thoracic Cancer","volume":"16 13","pages":"e70115"},"PeriodicalIF":2.3000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224037/pdf/","citationCount":"0","resultStr":"{\"title\":\"Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience.\",\"authors\":\"Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli\",\"doi\":\"10.1111/1759-7714.70115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Nonsmall cell lung cancer (NSCLC) remains a significant global health burden, necessitating advancements in diagnostic and prognostic strategies. Liquid biopsy and radiomics offer promising avenues for enhancing preoperative assessment and treatment planning in NSCLC.</p><p><strong>Methods: </strong>This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)-guided biopsy and liquid biopsy. Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations.</p><p><strong>Results: </strong>Among 57 patients with available data, associations between radiomic features and gene pairs mutation obtained from liquid biopsy exhibited moderate accuracy (approximately 0.60), with texture features demonstrating higher importance. However, when predicting the combined mutation status of gene pairs (e.g., EGFR and ROS1), the classification task involved three classes and yielded substantially lower accuracy (approximately 0.30), likely due to class imbalance and increased complexity.</p><p><strong>Discussion: </strong>Our findings demonstrate a moderate association between radiomic features and single gene mutations detected through liquid biopsy in NSCLC patients, with classification accuracies reaching approximately 0.60. In contrast, classification performance significantly declined (to ~0.30) when gene mutation pairs were used as targets, likely due to increased complexity and class imbalance. Notably, second-order texture features showed the highest importance in the models. These preliminary results suggest that radiomics may capture aspects of tumor biology reflected in liquid biopsy, warranting further validation in larger, well-balanced cohorts.</p><p><strong>Conclusion: </strong>The integration of liquid biopsy and radiomics holds promise for enhancing preoperative assessment and personalized treatment strategies in NSCLC. Further research on larger cohorts is warranted to validate the findings and translate them into clinical practice.</p><p><strong>Trial registration: </strong>University of Campania Trial Board UC20201112-24997.</p>\",\"PeriodicalId\":23338,\"journal\":{\"name\":\"Thoracic Cancer\",\"volume\":\"16 13\",\"pages\":\"e70115\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224037/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Thoracic Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/1759-7714.70115\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thoracic Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/1759-7714.70115","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience.
Background: Nonsmall cell lung cancer (NSCLC) remains a significant global health burden, necessitating advancements in diagnostic and prognostic strategies. Liquid biopsy and radiomics offer promising avenues for enhancing preoperative assessment and treatment planning in NSCLC.
Methods: This prospective study enrolled 60 NSCLC patients who underwent both computed tomography (CT)-guided biopsy and liquid biopsy. Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations.
Results: Among 57 patients with available data, associations between radiomic features and gene pairs mutation obtained from liquid biopsy exhibited moderate accuracy (approximately 0.60), with texture features demonstrating higher importance. However, when predicting the combined mutation status of gene pairs (e.g., EGFR and ROS1), the classification task involved three classes and yielded substantially lower accuracy (approximately 0.30), likely due to class imbalance and increased complexity.
Discussion: Our findings demonstrate a moderate association between radiomic features and single gene mutations detected through liquid biopsy in NSCLC patients, with classification accuracies reaching approximately 0.60. In contrast, classification performance significantly declined (to ~0.30) when gene mutation pairs were used as targets, likely due to increased complexity and class imbalance. Notably, second-order texture features showed the highest importance in the models. These preliminary results suggest that radiomics may capture aspects of tumor biology reflected in liquid biopsy, warranting further validation in larger, well-balanced cohorts.
Conclusion: The integration of liquid biopsy and radiomics holds promise for enhancing preoperative assessment and personalized treatment strategies in NSCLC. Further research on larger cohorts is warranted to validate the findings and translate them into clinical practice.
Trial registration: University of Campania Trial Board UC20201112-24997.
期刊介绍：
Thoracic Cancer aims to facilitate international collaboration and exchange of comprehensive and cutting-edge information on basic, translational, and applied clinical research in lung cancer, esophageal cancer, mediastinal cancer, breast cancer and other thoracic malignancies. Prevention, treatment and research relevant to Asia-Pacific is a focus area, but submissions from all regions are welcomed. The editors encourage contributions relevant to prevention, general thoracic surgery, medical oncology, radiology, radiation medicine, pathology, basic cancer research, as well as epidemiological and translational studies in thoracic cancer. Thoracic Cancer is the official publication of the Chinese Society of Lung Cancer, International Chinese Society of Thoracic Surgery and is endorsed by the Korean Association for the Study of Lung Cancer and the Hong Kong Cancer Therapy Society.
The Journal publishes a range of article types including: Editorials, Invited Reviews, Mini Reviews, Original Articles, Clinical Guidelines, Technological Notes, Imaging in thoracic cancer, Meeting Reports, Case Reports, Letters to the Editor, Commentaries, and Brief Reports.
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