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

被引:0
|
作者
Zhou, Mi [1 ]
Huang, Hongyun [2 ]
Bao, Deying [2 ]
Chen, Meining [3 ]
机构
[1] Sichuan Prov Orthoped Hosp, Dept Radiol, Chengdu 610041, Peoples R China
[2] Univ Elect Sci & Technol China, Sichuan Prov Peoples Hosp, Dept Radiol, Chengdu 610072, Peoples R China
[3] Siemens Healthineers, Dept MR Sci Mkt, Shanghai 200135, Peoples R China
关键词
Histogram analysis; Rectal cancer; Neoadjuvant treatment; Fractional order calculus; Tumor response; RADIATION-THERAPY; ANOMALOUS DIFFUSION; TUMOR RESPONSE; CHEMORADIOTHERAPY; DIFFERENTIATION; CHEMORADIATION; OUTCOMES;
D O I
10.1016/j.clinimag.2024.110327
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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 (beta), spatial parameter (mu), and the standard apparent diffusion coefficient (ADC), were calculated. Changes in median values (Delta X-median) and ratio (r Delta X-median) were calculated. Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance. Results: Pre-treatment beta-10th percentile values were significantly lower in the pCR group compared to the non-pCR group (p < 0.001). The Delta beta-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.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Morphologic predictors of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
    Zhang, Chongda
    Ye, Feng
    Liu, Yuan
    Ouyang, Han
    Zhao, Xinming
    Zhang, Hongmei
    ONCOTARGET, 2018, 9 (04) : 4862 - 4874
  • [2] Assessing pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review
    Ryan, J. E.
    Warrier, S. K.
    Lynch, A. C.
    Heriot, A. G.
    COLORECTAL DISEASE, 2015, 17 (10) : 849 - 861
  • [3] Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review
    Ryan, J. E.
    Warrier, S. K.
    Lynch, A. C.
    Ramsay, R. G.
    Phillips, W. A.
    Heriot, A. G.
    COLORECTAL DISEASE, 2016, 18 (03) : 234 - 246
  • [4] A dynamic nomogram for predicting pathologic complete response to neoadjuvant chemotherapy in locally advanced rectal cancer
    Wang, Guancong
    Li, Jiasen
    Huang, Ying
    Guo, Yincong
    CANCER MEDICINE, 2024, 13 (11):
  • [5] Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response
    Cho, Seung Hyun
    Kim, Gab Chul
    Jang, Yun-Jin
    Ryeom, Hunkyu
    Kim, Hye Jung
    Shin, Kyung-Min
    Park, Jun Seok
    Choi, Gyu-Seog
    Kim, See Hyung
    ACTA RADIOLOGICA, 2015, 56 (09) : 1042 - 1050
  • [6] Treatment Intensification for Locally Advanced Rectal Cancer: Impact on Pathological Complete Response and Outcomes
    Di Tommaso, Monica
    Rosa, Consuelo
    Caravatta, Luciana
    Augurio, Antonietta
    Borzillo, Valentina
    Di Santo, Sara
    Perrotti, Francesca
    Taraborrelli, Maria
    Cianci, Roberta
    Innocenti, Paolo
    Di Sebastiano, Pierluigi
    Colasante, Antonella
    Angelucci, Domenico
    Basti, Massimo
    Sindici, Giulia
    Mazzola, Lorenzo
    Pizzicannella, Giuseppe
    Di Bartolomeo, Nicola
    Marchioni, Michele
    Di Nicola, Marta
    Genovesi, Domenico
    IN VIVO, 2020, 34 (03): : 1223 - 1233
  • [7] MRI Evaluation of Complete and Near-Complete Response after Neoadjuvant Therapy in Patients with Locally Advanced Rectal Cancer
    Popita, Anca-Raluca
    Lisencu, Cosmin
    Rusu, Adriana
    Popita, Cristian
    Cainap, Calin
    Irimie, Alexandru
    Resiga, Liliana
    Munteanu, Alina
    Fekete, Zsolt
    Badea, Radu
    DIAGNOSTICS, 2022, 12 (04)
  • [8] Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
    Liu, Zhenyu
    Zhang, Xiao-Yan
    Shi, Yan-Jie
    Wang, Lin
    Zhu, Hai-Tao
    Tang, Zhenchao
    Wang, Shuo
    Li, Xiao-Ting
    Tian, Jie
    Sun, Ying-Shi
    CLINICAL CANCER RESEARCH, 2017, 23 (23) : 7253 - 7262
  • [9] Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy
    Huang, Chun-Ming
    Huang, Ming-Yii
    Huang, Ching-Wen
    Tsai, Hsiang-Lin
    Su, Wei-Chih
    Chang, Wei-Chiao
    Wang, Jaw-Yuan
    Shi, Hon-Yi
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [10] Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
    Lou, Xiaoying
    Zhou, Niyun
    Feng, Lili
    Li, Zhenhui
    Fang, Yuqi
    Fan, Xinjuan
    Ling, Yihong
    Liu, Hailing
    Zou, Xuan
    Wang, Jing
    Huang, Junzhou
    Yun, Jingping
    Yao, Jianhua
    Huang, Yan
    FRONTIERS IN ONCOLOGY, 2022, 12