Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer.

IF 5.1 4区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Precision Clinical Medicine Pub Date : 2021-04-30 eCollection Date: 2021-06-01 DOI:10.1093/pcmedi/pbab011

Zeyan Xu, Ke Zhao, Lujun Han, Pinxiong Li, Zhenwei Shi, Xiaomei Huang, Chu Han, Huihui Wang, Minglei Chen, Chen Liu, Yanting Liang, Suyun Li, Yanqi Huang, Xin Chen, Changhong Liang, Wuteng Cao, Zaiyi Liu

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引用次数: 0

Abstract

Background: Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer.

Methods: Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent pre-operative rectal MRI from three hospitals were retrospectively enrolled. All patients were divided into the primary cohort (N = 84) and validation cohort (N = 51). Quantitative image analysis was performed on T1-weighted (T1WI), T2-weighted (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The subjective qualitative MRI findings were evaluated by two radiologists in consensus. Multivariable analysis was performed using stepwise logistic regression. The discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI).

Results: The skewness derived from T2WI (T2WI-skewness) showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer (primary cohort: AUC = 0.852, 95% CI 0.788-0.916; validation cohort: 0.730, 0.645-0.815). Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors, and incorporating both factors achieved good discrimination performance in two cohorts (primary cohort: AUC = 0.913, 95% CI 0.868-0.958; validation cohort: 0.902, 0.844-0.960).

Conclusions: Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer. The quantitative image analysis helps improve diagnostic accuracy.

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结合定量和定性磁共振成像特征，区分肛门直肠恶性黑色素瘤和低位直肠癌。

背景：肛门直肠恶性黑色素瘤与低位直肠癌的鉴别仍具有挑战性，因为临床症状与影像学结果存在重叠。我们旨在研究结合定量和定性磁共振成像（MRI）特征能否区分肛门直肠恶性黑色素瘤和低位直肠癌：回顾性地选取了三家医院的 37 名肛门直肠恶性黑色素瘤患者和 98 名术前接受直肠磁共振成像检查的低位直肠癌患者。所有患者被分为主要队列（84 人）和验证队列（51 人）。对 T1 加权（T1WI）、T2 加权（T2WI）和对比增强 T1 加权成像（CE-T1WI）进行了定量图像分析。核磁共振成像的主观定性结果由两名放射科医生共同评估。采用逐步逻辑回归法进行多变量分析。判别性能通过接收者操作特征曲线下面积（AUC）和 95% 置信区间（CI）进行评估：在区分肛门直肠恶性黑色素瘤和低位直肠癌的所有定量图像特征中，T2WI（T2WI-skewness）得出的偏斜度显示出最佳的鉴别性能（原始队列：AUC = 0.852，95% CI 0.788-0.916；验证队列：0.730，0.645-0.815）。多变量分析表明，T2WI斜度和T1WI信号强度是独立的因素，在两个队列中纳入这两个因素可获得良好的分辨性能（主要队列：AUC = 0.913，95% CI 0.868-0.958；验证队列：0.902，0.844-0.960）：结论：结合T2WI斜度和T1WI信号强度对肛门直肠恶性黑色素瘤和低位直肠癌进行鉴别具有良好的效果。定量图像分析有助于提高诊断的准确性。

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

Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-

CiteScore

10.80

自引率

0.00%

发文量

审稿时长

5 weeks

期刊介绍： Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.