Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer

IF 1.8 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Clinical Imaging Pub Date : 2024-10-20 DOI:10.1016/j.clinimag.2024.110327

Mi Zhou , Hongyun Huang , Deying Bao , Meining Chen

{"title":"Fractional order calculus model-derived histogram metrics for assessing pathological complete response to neoadjuvant chemotherapy in locally advanced rectal cancer","authors":"Mi Zhou , Hongyun Huang , Deying Bao , Meining Chen","doi":"10.1016/j.clinimag.2024.110327","DOIUrl":null,"url":null,"abstract":"<div><h3>Aim</h3><div>This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes.</div></div><div><h3>Materials and methods</h3><div>Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX<sub>-median</sub>) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance.</div></div><div><h3>Results</h3><div>Pre-treatmentβ<sub>-10th</sub> percentile values were significantly lower in the pCR group compared to the non-pCR group (<em>p</em> < 0.001). The Δβ<sub>-median</sub> showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %).</div></div><div><h3>Discussion</h3><div>The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"116 ","pages":"Article 110327"},"PeriodicalIF":1.8000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0899707124002572","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Aim

This study evaluates the value of diffusion fractional order calculus (FROC) model for the assessment of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes.

Materials and methods

Ninety-eight patients were prospectively included. Every patient received MRI scans before and after nCRT using a 3.0-Tesla MRI machine. Parameters of the FROC model, including the anomalous diffusion coefficient (D), intravoxel diffusion heterogeneity (β), spatial parameter (μ), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (ΔX-median) and ratio (rΔX_-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance.

Results

Pre-treatmentβ_-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Δβ_-median showed higher diagnostic accuracy (AUC = 0.870) and sensitivity (76.67 %) for predicting tumor response compared to MRI tumor regression grading (mrTRG) scores (AUC = 0.722; sensitivity = 90.0 %).

Discussion

The use of FROC alongside comprehensive tumor histogram analysis was found to be practical and effective in evaluating the tumor response to nCRT in LARC patients.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

用于评估局部晚期直肠癌新辅助化疗病理完全反应的分数阶微积分模型衍生直方图指标。

目的：本研究评估了扩散分数阶微积分（FROC）模型在局部晚期直肠癌（LARC）新辅助化放疗（nCRT）后病理完全反应（pCR）评估中的价值，该模型使用了从全肿瘤体积得出的直方图分析：前瞻性纳入98例患者。每位患者在接受 nCRT 治疗前后均使用 3.0 特斯拉核磁共振成像仪接受了核磁共振成像扫描。计算FROC模型的参数，包括异常扩散系数（D）、体内扩散异质性（β）、空间参数（μ）和标准表观扩散系数（ADC）。计算中值（ΔX-中值）和比值（rΔX-中值）的变化。采用接收者操作特征曲线（ROC）评估诊断效果：结果：与 MRI 肿瘤回归分级（mrTRG）评分（AUC = 0.722；灵敏度 = 90.0 %）相比，pCR 组治疗前β-10 百分位数值明显低于非CR 组（p -median 在预测肿瘤反应方面显示出更高的诊断准确性（AUC = 0.870）和灵敏度（76.67 %）：讨论：在评估LARC患者的肿瘤对nCRT的反应时，发现使用FROC与综合肿瘤直方图分析一起使用既实用又有效。

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分钟内获得全文去求助

来源期刊

Clinical Imaging 医学-核医学

CiteScore

4.60

自引率

0.00%

发文量

265

审稿时长

35 days

期刊介绍： The mission of Clinical Imaging is to publish, in a timely manner, the very best radiology research from the United States and around the world with special attention to the impact of medical imaging on patient care. The journal''s publications cover all imaging modalities, radiology issues related to patients, policy and practice improvements, and clinically-oriented imaging physics and informatics. The journal is a valuable resource for practicing radiologists, radiologists-in-training and other clinicians with an interest in imaging. Papers are carefully peer-reviewed and selected by our experienced subject editors who are leading experts spanning the range of imaging sub-specialties, which include: -Body Imaging- Breast Imaging- Cardiothoracic Imaging- Imaging Physics and Informatics- Molecular Imaging and Nuclear Medicine- Musculoskeletal and Emergency Imaging- Neuroradiology- Practice, Policy & Education- Pediatric Imaging- Vascular and Interventional Radiology

期刊最新文献

Contents A qualitative, community-based needs assessment to inform the development of a radiology health equity curriculum Prompting large language models for inner gains in radiology studies Evaluating large language models in supporting bone-RADS scoring: accuracy and inter-model agreement Contents