Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2024-07-27 DOI:10.1002/mp.17330

Feng Wen, Jie Zhou, Zhebin Chen, Meng Dou, Yu Yao, Xin Wang, Feng Xu, Yali Shen

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引用次数: 0

Abstract

Background

While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive.

Purpose

The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers.

Methods

Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (n = 120) and testing set (n = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score.

Results

In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1–3 by two expert radiation oncologists, respectively, meaning only minor edits needed.

Conclusions

The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.

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基于深度学习的盆腔恶性肿瘤选择性淋巴结区域划分的高效应用

背景虽然国际上已就盆腔淋巴结区域（LNR）的划分达成共识，但观察者之间和观察者内部仍存在显著差异。本研究的目的是为盆腔癌症患者建立盆腔 LNRs 划线的深度学习模型。方法回顾性收集了 160 名盆腔原发性恶性肿瘤（包括直肠癌、前列腺癌和宫颈癌）患者的计划计算机断层扫描（CT）研究结果，并将其分为训练集（n = 120）和测试集（n = 40）。两位放射肿瘤专家划定了六个盆腔 LNR，包括腹部骶前结节、盆腔骶前结节、髂内结节、髂外结节、钝结节和腹股沟结节，作为地面实况（Gt）轮廓。级联多头 U 网（CMU-net）是根据训练队列中的 Gt 轮廓构建的，随后在测试队列中得到验证。使用骰子相似系数（DSC）、平均表面距离（ASD）、第 95 百分位数豪斯多夫距离（HD95）和 7 点量表评分对六个 LNR 的自动划定（Auto）进行了评估。结果在测试组中，CMU-网模型对六个盆腔 LNR 的 DSC 从 0.851 到 0.942 不等，ASD 从 0.381 到 1.037 毫米不等，HD95 从 2.025 到 3.697 毫米不等。术后和术前病例的这三个参数没有明显差异。两位放射肿瘤专家分别对 CMU-net 模型 95.9% 和 96.2% 的自动划线结果打了 1-3 分，这意味着只需稍作修改即可。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.