Association of artificial intelligence-screened interstitial lung disease with radiation pneumonitis in locally advanced non-small cell lung cancer

IF 5.3 1区医学 Q1 ONCOLOGY

Radiotherapy and Oncology Pub Date : 2025-09-13 DOI:10.1016/j.radonc.2025.111144

Hannah Bacon , Nicholas McNeil , Tirth Patel , Mattea Welch , Xiang Y. Ye , Andrea Bezjak , Benjamin H. Lok , Srinivas Raman , Meredith Giuliani , B.C. John Cho , Alexander Sun , Patricia Lindsay , Geoffrey Liu , Sonja Kandel , Chris McIntosh , Tony Tadic , Andrew Hope

{"title":"Association of artificial intelligence-screened interstitial lung disease with radiation pneumonitis in locally advanced non-small cell lung cancer","authors":"Hannah Bacon , Nicholas McNeil , Tirth Patel , Mattea Welch , Xiang Y. Ye , Andrea Bezjak , Benjamin H. Lok , Srinivas Raman , Meredith Giuliani , B.C. John Cho , Alexander Sun , Patricia Lindsay , Geoffrey Liu , Sonja Kandel , Chris McIntosh , Tony Tadic , Andrew Hope","doi":"10.1016/j.radonc.2025.111144","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>Interstitial lung disease (ILD) has been correlated with an increased risk for radiation pneumonitis (RP) following lung SBRT, but the degree to which locally advanced NSCLC (LA-NSCLC) patients are affected has yet to be quantified. An algorithm to identify patients at high risk for RP may help clinicians mitigate risk.</div></div><div><h3>Methods</h3><div>All LA-NSCLC patients treated with definitive radiotherapy at our institution from 2006 to 2021 were retrospectively assessed. A convolutional neural network was previously developed to identify patients with radiographic ILD using planning computed tomography (CT) images<em>.</em> All screen-positive (AI-ILD + ) patients were reviewed by a thoracic radiologist to identify true radiographic ILD (r-ILD). The association between the algorithm output, clinical and dosimetric variables, and the outcomes of grade ≥3 RP and mortality were assessed using univariate (UVA) and multivariable (MVA) logistic regression, and Kaplan-Meier survival analysis.</div></div><div><h3>Results</h3><div>698 patients were included in the analysis. Grade (G) 0–5 RP was reported in 51 %, 27 %, 17 %, 4.4 %, 0.14 % and 0.57 % of patients, respectively. Overall, 23 % of patients were classified as AI-ILD+. On MVA, only AI-ILD status (OR 2.15, p = 0.03) and AI-ILD score (OR 35.27, p < 0.01) were significant predictors of G3+RP. Median OS was 3.6 years in AI-ILD- patients and 2.3 years in AI-ILD+patients (NS). Patients with r-ILD had significantly higher rates of severe toxicities, with G3+RP 25 % and G5 RP 7 %. R-ILD was associated with an increased risk for G3+RP on MVA (OR 5.42, p < 0.01).</div></div><div><h3>Conclusion</h3><div>Our AI-ILD algorithm detects patients with significantly increased risk for G3+RP.</div></div>","PeriodicalId":21041,"journal":{"name":"Radiotherapy and Oncology","volume":"212 ","pages":"Article 111144"},"PeriodicalIF":5.3000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiotherapy and Oncology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167814025046481","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose

Interstitial lung disease (ILD) has been correlated with an increased risk for radiation pneumonitis (RP) following lung SBRT, but the degree to which locally advanced NSCLC (LA-NSCLC) patients are affected has yet to be quantified. An algorithm to identify patients at high risk for RP may help clinicians mitigate risk.

Methods

All LA-NSCLC patients treated with definitive radiotherapy at our institution from 2006 to 2021 were retrospectively assessed. A convolutional neural network was previously developed to identify patients with radiographic ILD using planning computed tomography (CT) images. All screen-positive (AI-ILD + ) patients were reviewed by a thoracic radiologist to identify true radiographic ILD (r-ILD). The association between the algorithm output, clinical and dosimetric variables, and the outcomes of grade ≥3 RP and mortality were assessed using univariate (UVA) and multivariable (MVA) logistic regression, and Kaplan-Meier survival analysis.

Results

698 patients were included in the analysis. Grade (G) 0–5 RP was reported in 51 %, 27 %, 17 %, 4.4 %, 0.14 % and 0.57 % of patients, respectively. Overall, 23 % of patients were classified as AI-ILD+. On MVA, only AI-ILD status (OR 2.15, p = 0.03) and AI-ILD score (OR 35.27, p < 0.01) were significant predictors of G3+RP. Median OS was 3.6 years in AI-ILD- patients and 2.3 years in AI-ILD+patients (NS). Patients with r-ILD had significantly higher rates of severe toxicities, with G3+RP 25 % and G5 RP 7 %. R-ILD was associated with an increased risk for G3+RP on MVA (OR 5.42, p < 0.01).

Conclusion

Our AI-ILD algorithm detects patients with significantly increased risk for G3+RP.

查看原文本刊更多论文

人工智能筛查间质性肺病与局部晚期非小细胞肺癌放射性肺炎的关系

目的：间质性肺疾病（ILD）与肺SBRT后放射性肺炎（RP）风险增加相关，但局部晚期NSCLC （LA-NSCLC）患者受影响的程度尚未量化。一种识别RP高风险患者的算法可以帮助临床医生降低风险。方法：回顾性评估2006年至2021年在我院接受明确放疗的所有LA-NSCLC患者。卷积神经网络先前被开发用于通过规划计算机断层扫描（CT）图像识别放射学ILD患者。所有筛查阳性（AI-ILD + ）的患者由胸科放射科医生检查，以确定真正的放射学ILD （r-ILD）。使用单变量（UVA）和多变量（MVA）逻辑回归以及Kaplan-Meier生存分析评估算法输出、临床和剂量学变量以及 ≥ 3rp和死亡率之间的关联。结果：698例患者纳入分析。0-5级RP分别在51 %、27 %、17 %、4.4 %、0.14 %和0.57 %的患者中报道。总体而言，23% %的患者被分类为AI-ILD + 。在MVA上，只有AI-ILD状态（OR 2.15, p = 0.03）和AI-ILD评分（OR 35.27, p ）。结论：我们的AI-ILD算法检测出G3 + RP风险显著增加的患者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Radiotherapy and Oncology 医学-核医学

CiteScore

10.30

自引率

10.50%

发文量

2445

审稿时长

45 days

期刊介绍： Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.