Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program

Tariq

et al. 2022

Journal of Oncology

Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model’s good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

Section: Discussionmentioning

confidence: 99%

Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network

Tariq

et al. 2022

Journal of Oncology

“…This DL-CAD system has the ability to assist radiologists in nodule detection in LDCT lung cancer screening. 31…”

Section: Study Reviewsmentioning

confidence: 99%

Noninvasive Technique for Classification of Pulmonary Cancer Based on Computerized Tomography: Review and Analysis

Osman,

Hamza,

Mohamed

et al. 2024

J Clin Eng

Lung cancer is one of the leading causes of death in the United States and Europe. Most cancers that start in the lung, known as primary lung cancers, are carcinomas that derive from epithelial cells. The most common symptoms are coughing (including coughing up blood), weight loss, shortness of breath, and chest pains. Different diagnostic procedures have been followed in an attempt to differentiate between the benign and malignant tumors such as computed tomography or magnetic resonance imaging scans, angiogram, chest x-ray, and biopsy, which is the most accurate procedure that determines the pulmonary tumor type. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterized abnormalities that are found. In the clinical interpretation of medical images, limitations in the human eye-brain visual system, reader fatigue, distraction, the presence of overlapping structures that camouflage disease in images, and the vast number of abnormal cases seen in screening programs provide cause for detection and interpretation errors. A comprehensive review on the subject has been done to give an overview of the recent studies, their methodology, and the result of those studies, along with the authors' analysis of these studies.

“…ESTI has issued specific recommendations and prepared webinars and workshops for use in CAD support in reading a LC screening test ( ). Moreover, recent studies based on deep learning algorithms for automatic CAD detection of lung nodules yielded very promising results [ 65 , 66 , 67 ]. Since 2019, ESTI has provided certification courses for education of radiologists in LC screening.…”

Section: Screening Chest Low-dose Ct and Ct Colonographymentioning

confidence: 99%

Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?

Mascalchi

Picozzi²,

Puliti³

et al. 2022

Diagnostics

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.