Comparing three-dimensional and two-dimensional deep-learning, radiomics, and fusion models for predicting occult lymph node metastasis in laryngeal squamous cell carcinoma based on CT imaging: a multicentre, retrospective, diagnostic study

Abstract

Background: The occult lymph node metastasis (LNM) of laryngeal squamous cell carcinoma (LSCC) affects the treatment and prognosis of patients. This study aimed to comprehensively compare the performance of the three-dimensional and two-dimensional deep learning models, radiomics model, and the fusion models for predicting occult LNM in LSCC.

Methods: In this retrospective diagnostic study, a total of 553 patients with clinical N0 stage LSCC, who underwent surgical treatment without distant metastasis and multiple primary cancers, were consecutively enrolled from four Chinese medical centres between January 01, 2016 and December 30, 2020. The participant data were manually retrieved from medical records, imaging databases, and pathology reports. The study cohort was divided into a training set (n = 300), an internal test set (n = 89), and two external test sets (n = 120 and 44, respectively). The three-dimensional deep learning (3D DL), two-dimensional deep learning (2D DL), and radiomics model were developed using CT images of the primary tumor. The clinical model was constructed based on clinical and radiological features. Two fusion strategies were utilized to develop the fusion model: the feature-based DLRad_FB model and the decision-based DLRad_DB model. The discriminative ability and correlation of 3D DL, 2D DL and radiomics features were analysed comprehensively. The performances of the predictive models were evaluated based on the pathological diagnosis.

Findings: The 3D DL features had superior discriminative ability and lower internal redundancy compared to 2D DL and radiomics features. The DLRad_DB model achieved the highest AUC (0.89-0.90) among all the study sets, significantly outperforming the clinical model (AUC = 0.73-0.78, P = 0.0001-0.042, Delong test). Compared to the DLRad_DB model, the AUC values for the DLRad_FB, 3D DL, 2D DL, and radiomics models were 0.82-0.84 (P = 0.025-0.46), 0.86-0.89 (P = 0.75-0.97), 0.83-0.86 (P = 0.029-0.66), and 0.79-0.82 (P = 0.0072-0.10), respectively in the study sets. Additionally, the DLRad_DB model exhibited the best sensitivity (82-88%) and specificity (79-85%) in the test sets.

Interpretation: The decision-based fusion model DLRad_DB, which combines 3D DL, 2D DL, radiomics, and clinical data, can be utilized to predict occult LNM in LSCC. This has the potential to minimize unnecessary lymph node dissection and prophylactic radiotherapy in patients with cN0 disease.

Funding: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province.

Keywords: Artificial intelligence; Deep learning; Laryngeal cancer; Occult lymph node metastasis; Radiomics.

Fig. 1

Flowchart diagram shows the patient selection process from four medical centres. LSCC indicates laryngeal squamous cell carcinoma; PKUPH, Peking University People’s Hospital; QFS, Shandong Provincial Qianfoshan Hospital; pN (+), pathological lymph node positive; pN (−), pathological lymph node negative; and cN0, clinical N0 stage.

Fig. 2

Workflow diagram for the development of the predictive models. Tumor segmentation and region of interest (ROI) delineation are performed by experienced radiologists. The Radiomics model is developed using PyRadiomics. For the two-dimensional deep learning (2D DL) model, the pre-trained ImageNet ResNet50 is fine-tuned based on our training data. The tumor's maximal ROI cross-section and six adjacent CT slices above and below it, are cropped as the input for ResNet50. The patient-level probability is calculated by averaging the probabilities of all CT slices belonging to one patient. For the three-dimensional deep learning (3D DL) model, the pre-trained 3D ResNet50 backbone is retained and transferred to optimize parameters in our training data. The clinical data and radiological features are used to construct the clinical model. For the early fusion model, the extracted features from four basic models are combined to train an SVM classifier. For the late fusion model, the output probabilities from four basic models are used to develop a stacking model with a random forest classifier.

Fig. 3

Discrimination ability and correlation analysis of radiomics and deep learning (DL) features in the external test set 1. T-distributed stochastic neighbour embedding (t-SNE) visualizations for the radiomics features (A), two-dimensional (2D) DL features (B), and three-dimensional (3D) DL features (C) in the external test set 1. Each dot represents a patient. Blue dots indicate patients with lymph node metastasis (LNM), and red dots indicate patients without LNM. Hierarchical clustering heatmap for key radiomics features (D), top 2D DL features (E), and top 3D DL features (F) in the external test set 1. The x-axis represents the IDs of radiomics and DL features, and the y-axis represents 120 patients. Patients belong to the same cluster (adjacent rows) share similar features in the Euclidean space. The status of LNM is displayed on the white-green bar located on the left side next to the y-axis. Supplementary Table S2 provides the descriptions of the key radiomics features. (G) The correlation heatmap of key radiomics features, top 2D and 3D DL features in external test set 1, where the color represents the magnitude of the spearmen correlation coefficient, and the asterisks indicate P < 0.05. Red indicates radiomics.

Fig. 4

Performances for occult lymph node metastasis (LNM) prediction. The receiver operating characteristic (ROC) curves of the DLRad_DB model, three-dimensional (3D) deep learning (DL) model, two-dimensional (2D) DL model, radiomics model, and clinical model in the training set (A), internal test set (B), external test set 1 (C), and external test set 2 (D). AUC indicates area under the curve.

Fig. 5

Performances for occult lymph node metastasis (LNM) prediction. The receiver operator characteristic (ROC) curves of the DLRad_DB model and the DLRad_FB model in the training set (A), internal test set (B), external test set 1 (C), and external test set 2 (D). AUC indicates area under the curve. P was calculated through the Delong test.