Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma

作者全名："Huo, Jiwen; Min, Xuhong; Luo, Tianyou; Lv, Fajin; Feng, Yibo; Fan, Qianrui; Wang, Dawei; Ma, Dongchun; Li, Qi"

作者地址："[Huo, Jiwen; Luo, Tianyou; Lv, Fajin; Li, Qi] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Min, Xuhong; Ma, Dongchun] Anhui Chest Hosp, 397 Jixi Rd, Hefei 230022, Anhui, Peoples R China; [Feng, Yibo; Fan, Qianrui; Wang, Dawei] Infervis Med Technol Co Ltd, Inst Res, Yuanyang Int Ctr, 25F Bldg E, Beijing 100025, Peoples R China"

通信作者："Li, Q (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China.; Ma, DC (通讯作者)，Anhui Chest Hosp, 397 Jixi Rd, Hefei 230022, Anhui, Peoples R China."

来源：RADIOLOGIA MEDICA

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001190244400017

JCR分区：Q1

影响因子：9.7

年份：2024

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Lung cancer; Adenocarcinoma; Tomography; X-ray computed; Deep learning; Pathology

摘要："PurposeTo investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).Materials and MethodsFrom June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in <= 2-cm-diameter ILADC.ResultsFor model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models.ConclusionThe CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors."

基金机构：Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau)

基金资助正文：No Statement Available