A Dual-Modality Ultrasound Video Recognition Model for Distinguishing Subpleural Pulmonary Nodules

Abstract

Objective

To develop a deep learning model based on dual-modality ultrasound (DMUS) video recognition for the differential diagnosis of benign and malignant subpleural pulmonary nodules (SPNs).

Patients and Methods

Participant data (n=193, median age, 58 years [IQR, 34-66 years]; 123 men) with SPNs, prospectively collected from January 7, to December 21, 2020, were divided into training (n=154) and validation (n=39) sets in an 8:2 ratio. Additionally, independent internal (n=88) and external (n=91) test sets were prospectively collected from January 10 to June 25, 2021. The nature of the SPNs was determined through biopsy (n=306) and clinical follow-up (n=66). Our model integrated DMUS videos, time-intensity curves, and clinical information. The model’s performance was evaluated using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity and compared with state-of-the-art video classification models, as well as ultrasound and computed tomography diagnoses made by radiologists.

Results

In the internal test set, our model accurately distinguished malignant from benign SPNs with an AUC, accuracy, sensitivity, and specificity of 0.91, 91% (80 of 88), 90% (27 of 30), and 91% (53 of 58), outperforming state-of-the-art video classification models (all P<.05). In the external test set, the model achieved the accuracy, sensitivity, and specificity of 89% (81 of 91), 84% (27 of 32), and 92% (54 of 59), which were higher than the parameters for radiologist interpretations of ultrasound (81% [74 of 91], 63% [20 of 32], and 92% [54 of 59]) and computed tomography (76% [69 of 91], 91% [29 of 32], and 68% [40 of 59]), respectively.

Conclusion

This deep learning model based on DMUS video recognition enhances the performance of ultrasound in differentiating benign from malignant SPNs.

Trial Registration

clinicaltrials.gov Identifier: ChiCTR1800019828