Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Abstract

Dutch-Belgian Lung Cancer Screening trial showed that screening high-risk individuals with low-dose chest CT reduced lung cancer mortality by 20% and 26%, respectively (1,2). This is linked to a beneficial stage shift, with stage I and II lung cancer having a much better prognosis than stage III or IV lung cancer (3). Lung cancer typically manifests as pulmonary nodules at CT. However, most nodules are benign and do not require further clinical workup. Nodule management guidelines and data-driven models have been developed to reduce the rate of false-positive findings and avoid overtreatment (4-8), but it remains a challenge to accurately distinguish between benign and malignant nodules (9).Deep learning (DL) with convolutional neural networks (CNNs) has recently become a method of choice for analyzing medical images (10). Several studies (11-13) showcased the potential of CNNs in predicting the malignancy risk of a pulmonary nodule by using the publicly available Lung Image Database Consortium image collection data set (14). However, these studies used the subjective labels provided by radiologists and lacked a solid reference standard set by histopathologic examination for malignant nodules and at least 2 years of imaging follow-up for benign nodules. Ardila et al (15) developed a DL algorithm that processes a whole CT image to predict patient-level malignancy risk. However, without risk scores for individual nodules, these algorithms are difficult to integrate as a second opinion in conjunction with current clinical guidelines like the Lung CT Screening Reporting and Data System (Lung-RADS) by the American College of Radiology (4,16). Another study evaluated a DL algorithm on two clinical data sets with a proven reference standard, but Background: Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening.Purpose: To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods:In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three cohorts collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts.

Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR

“…Among human–machine comparisons, many studies have reported that CNN diagnosis is on par with human visual assessment in multiple areas [ 17 ]. For example, for malignancy risk estimation of pulmonary nodules using thoracic CT, Venkadesh et al [ 18 ] reported that the DL algorithm had an AUC of 0.96, which was significantly better than the average AUC of the clinicians (0.90) but comparable to that of thoracic radiologists. Our model was able to discriminate between AL and TTR CA with interesting values of patient-based accuracy (0.711) and AUC (0.752); however, this was no better than the classification obtained with the simple measurement of the septal thickness, already reported in previous publications [ 19 , 20 , 21 ] and resulting from the known increased amyloid burden in this subtype.…”

Evaluation and Management of Indeterminate Pulmonary Nodules on Chest Computed Tomography in Asymptomatic Subjects: The Principles of Nodule Guidelines

“…Using only image voxels, deep learning algorithms were reported to show better performance than logistic regression-based methods. 48 49…”

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

“…In a complementary way, deep learning has already been used for the differential diagnosis of liver lesions [ 72 , 73 ], the detection of malignant lung nodules [ 74 , 75 ], the detection and classification of breast masses [ 76 ], and the assessment of lymph node metastases in women with breast cancer [ 77 ]. The methodology described before can be applied to multiple lesional imaging criteria.…”