Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Abstract

Importance: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.

Objective: To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer.

Design, setting, and participants: This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017.

Main outcomes and measures: The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis.

Results: A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001).

Conclusions and relevance: The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.

Figure 1.. Illustration of the DL Model and the Hybrid Model

The blue box on sagittal contrast-enhanced T1-weighted imaging (CET1WI) is a region of interest (ROI) tumor (tightly encapsulated tumor); the green box on sagittal CET1WI is an ROI tumor + peritumoral (5 pixels larger than the ROI tumor). Every 3 adjacent magnetic resonance imaging (MRI) sections were combined and scaled to 64 × 64 voxel size for deep learning (DL) analysis. The DL model consists of subnetworks 1 and 2, which are the stack of multiple convolutions, batch normalization, zero padding, and pooling layers. Feeding a tumor image, the DL model predicts the lymph node metastasis (LNM) probability (defined as DL score). The hybrid model consists of subnetworks 1 and 3, which integrate with the clinical variable (MRI-LN status). Feeding tumor images and the MRI-LN status of a patient, the hybrid model predicts the LNM probability at the end of subnetwork 3 (defined as H score).

Figure 2.. Performance of Various Models

Receiver operating characteristic (ROC) curves in the primary (A) and validation (B) cohorts of the contrast-enhanced T1-weighted imaging (CET1WI) tumor + peritumoral + clinical, CET1WI tumor + peritumoral, CET1WI tumor, apparent diffusion coefficient (ADC) tumor + peritumoral, T2-weighted imaging (T2WI) tumor + peritumoral, and clinical model. Survival curves according to the H score from the hybrid model with Kaplan-Meier (K-M) analysis in the primary (C) and validation (D) cohorts. DFS indicates disease-free survival.

Figure 3.. Representative Prediction Results From the Validation Cohort

The blue boxes on sagittal contrast-enhanced T1-weighted imaging (CET1WI) are region of interest (ROI) tumor, the green boxes on sagittal CET1WI and axial T2-weighted imaging (T2WI) are ROI tumor + peritumoral, and the yellow boxes on axial diffusion-weighted imaging (DWI) are lymph nodes. Positive magnetic resonance imaging (MRI)-reported lymph node (MRI-LN) status was assessed by the short-axis diameter of the largest lymph node larger than 10 mm. DL indicates deep learning; SCC, squamous cell carcinoma.