Yi Liu1, Ting Song2, Tian-Fa Dong2, Wei Zhang2, Ge Wen1. 1. Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China. 2. Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, PR China.
Abstract
BACKGROUND: Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. PURPOSE: To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. MATERIAL AND METHODS: A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. RESULTS: The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. CONCLUSION: MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.
BACKGROUND: Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. PURPOSE: To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. MATERIAL AND METHODS: A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. RESULTS: The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. CONCLUSION: MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.
Entities:
Keywords:
Radiomics; cervical carcinoma; clinicopathological characteristics; magnetic resonance imaging