Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

Abstract

Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).

Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve.

Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used.

Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

Keywords: Locally advanced cervical cancer; Magnetic resonance imaging; Neoadjuvant chemotherapy; Radiomics.

Fig. 1

Study design. Left block diagram is procedure of single sequence radiomic model construction. Right block diagram is procedure of combined model construction. T1WI, T1-weight imaging; T2WI, T2-weighted imaging.

Fig. 2

Feature expression maps for top radiomic features. (a) and (d) are Coif3_glszm_ low grey-level zone emphasis features of intratumoural T1WI of patients. (b) and (e) are Coif1_ngtdm_busyness features of features of intratumoural T2WI of patients. (c) and (f) are Coif3_ngtdm_complexity features of peritumoural T2WI of patients. T1WI, T1-weight imaging; T2WI, T2-weighted imaging.

Fig. 3

Comparison of ROC curves of different models. (a) and (b) are ROC curves of single sequence models in training and testing sets. (c) and (d) are ROC curves of combined models in training and testing sets. ROC receiver operating characteristic; AUC area under receiver operating characteristic curve; T1WI, T1-weight imaging; T2WI, T2-weighted imaging.

Fig. 4

Delong test between different models. P-value of Delong test between any two models. T1WI, T1-weighted imaging; T2WI, T2-weighted imaging.

Fig. 5

Comparison of ROC curves between different group of training and testing sets for combined models. (a) and (b) are ROC curves of combined model of intratumoural region on T1WI and intratumoural region on T2WI in training and testing sets. (c) and (d) are ROC curves of combined model of peritumoural and intratumoural zones on T2WI in training and testing sets. (e) and (f) are ROC curves of multi-sequence model in training and testing sets. ROC receiver operating characteristic; AUC area under receiver operating characteristic curve; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging.

Fig. 6

Comparison of ROC curves between clinical and radiomic models. ROC receiver operating characteristic; AUC area under receiver operating characteristic curve; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging.