The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.
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.
Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans. Six experienced chest radiologists independently determined which nodules to upgrade to category 4X, a recently introduced category for lesions that demonstrate additional features or imaging findings that increase the suspicion of malignancy. Malignancy rates of purely size-based categories and category 4X were compared. Furthermore, the false-positive rates of category 4X lesions were calculated and observer variability was assessed by using Fleiss κ statistics. Results The observers upgraded 15%-24% of the SSNs to category 4X. The malignancy rate for 4X nodules varied from 46% to 57% per observer and was substantially higher than the malignancy rates of categories 3, 4A, and 4B SSNs without observer intervention (9%, 19%, and 23%, respectively). On average, the false-positive rate for category 4X nodules was 7% for category 3 SSNs, 7% for category 4A SSNs, and 19% for category 4B SSNs. Of the falsely upgraded benign lesions, on average 27% were transient. The agreement among the observers was moderate, with an average κ value of 0.535 (95% confidence interval: 0.509, 0.561). Conclusion The inclusion of a 4X assessment category for lesions suspicious for malignancy in a nodule management tool is of added value and results in high malignancy rates in the hands of experienced radiologists. Proof of the transient character of category 4X lesions at short-term follow-up could avoid unnecessary invasive management. RSNA, 2017.
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