Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients?

Author:

Wu Qianyu123,Yi Yongju234,Lai Bingjia5,Li Jiao123,Lian Yanbang6,Chen Junhong7ORCID,Wu Yue123,Wang Xinhua123,Cao Wuteng123

Affiliation:

1. Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University , Guangzhou, Guangdong, P. R. China

2. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University , Guangzhou, Guangdong, P. R. China

3. Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China

4. Department of Information Center, The Sixth Affiliated Hospital, Sun Yat-sen University , Guangzhou, Guangdong, P. R. China

5. Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , Guangzhou, Guangdong, P. R. China

6. Department of Radiology, The First Affiliated Hospital of Zhengzhou University , Zhengzhou, Henan, P. R. China

7. School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University , Shenzhen, Guangdong, P. R. China

Abstract

Abstract Background Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT. Methods This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0–2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test. Results A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975–1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805–0.978) and 0.938 (95% CI, 0.840–0.985), respectively. Conclusions The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

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