Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma

• Published in: BMC women's health 2024-03, Vol.24 (1), p.182-182, Article 182
• Main Authors: Zhang, Xue-Fang, Wu, Hong-Yuan, Liang, Xu-Wei, Chen, Jia-Luo, Li, Jianpeng, Zhang, Shihao, Liu, Zhigang
• Format: Article
• Language: English
• Subjects: Adjuvant radiotherapy; Adjuvants; Analysis; Calibration; Cancer therapies; Cervical cancer; Cervical squamous cell carcinoma; Deep learning; Hysterectomy; Lymphatic system; Machine learning; Magnetic resonance imaging; Medical screening; Methods; Oncology; Pathology; Patient outcomes; Patients; Prevention; Radiation therapy; Radiomics; Radiotherapy; Risk factors; Squamous cell carcinoma; Surgery; Training; Tumors; Vagina; Womens health
• ISSN: 1472-6874; 1472-6874
• DOI: 10.1186/s12905-024-03001-6

Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P