预测非小细胞肺癌患者术后放疗获益的术前 CT 放射预后指数：一项多中心研究。

IF 3.5 2区医学 Q2 ONCOLOGY

Cancer Imaging Pub Date : 2024-05-13 DOI:10.1186/s40644-024-00707-6

Zeliang Ma, Yu Men, Yunsong Liu, Yongxing Bao, Qian Liu, Xu Yang, Jianyang Wang, Lei Deng, Yirui Zhai, Nan Bi, Luhua Wang, Zhouguang Hui

{"title":"预测非小细胞肺癌患者术后放疗获益的术前 CT 放射预后指数：一项多中心研究。","authors":"Zeliang Ma, Yu Men, Yunsong Liu, Yongxing Bao, Qian Liu, Xu Yang, Jianyang Wang, Lei Deng, Yirui Zhai, Nan Bi, Luhua Wang, Zhouguang Hui","doi":"10.1186/s40644-024-00707-6","DOIUrl":null,"url":null,"abstract":"Background: The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.Methods: Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.Results: In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.Conclusions: We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.","PeriodicalId":9548,"journal":{"name":"Cancer Imaging","volume":"24 1","pages":"61"},"PeriodicalIF":3.5000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089675/pdf/","citationCount":"0","resultStr":"{\"title\":\"Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.\",\"authors\":\"Zeliang Ma, Yu Men, Yunsong Liu, Yongxing Bao, Qian Liu, Xu Yang, Jianyang Wang, Lei Deng, Yirui Zhai, Nan Bi, Luhua Wang, Zhouguang Hui\",\"doi\":\"10.1186/s40644-024-00707-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.Methods: Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.Results: In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.Conclusions: We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.\",\"PeriodicalId\":9548,\"journal\":{\"name\":\"Cancer Imaging\",\"volume\":\"24 1\",\"pages\":\"61\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089675/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s40644-024-00707-6\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40644-024-00707-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

背景：非小细胞肺癌（NSCLC）患者术后放疗（PORT）的价值仍存在争议。一部分患者可能会从 PORT 中获益。我们的目标是确定可从 PORT 中获益的 NSCLC 患者：方法：纳入第一组和第二组病理Tany N2 M0 NSCLC患者，以及第三组至第六组非转移性NSCLC患者。放射学预后指数（RPI）是利用从第1组患者术前胸部CT扫描原发肺结节中提取的放射学纹理特征制定的，并在其他组别中进行了验证。我们采用了最小绝对收缩和选择算子-Cox 正则化模型进行数据降维、特征选择和 RPI 的构建。我们结合 RPI 和阳性淋巴结数（PLN）创建了淋巴放射预后指数（LRPI）。我们比较了根据 LRPI 确定的亚组中接受 PORT 和未接受 PORT 的患者的预后：在 1-6 组患者中，分别有 228、1003、144、422、19 和 21 名患者符合条件。RPI 预测了所有六个队列的总生存率（OS）：队列 1（HR = 2.31，95% CI：1.18-4.52）、队列 2（HR = 1.64，95% CI：1.26-2.14）、队列 3（HR = 2.53，95% CI：1.45-4.3），队列 4（HR = 1.24，95% CI：1.01-1.52），队列 5（HR = 2.56，95% CI：0.73-9.02），队列 6（HR = 2.30，95% CI：0.53-10.03）。LRPI 预测 OS（C 指数：0.68，95% CI：0.60-0.75）优于 pT 分期（C 指数：0.57，95% CI：0.50-0.63）、pT + PLN（C 指数：0.58，95% CI：0.46-0.70）和 RPI（C 指数：0.65，95% CI：0.54-0.75）。LRPI用于将患者分为三个风险组；中度风险组患者从PORT中获益（HR=0.60，95% CI：0.40-0.91；P=0.02），而低风险组和高风险组患者则没有获益：我们开发了基于 CT 的术前放射学和淋巴放射学预后指数，能够预测 NSCLC 患者的 OS 和 PORT 的益处。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.

Background: The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.

Methods: Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.

Results: In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.

Conclusions: We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

CiteScore

7.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.