Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

Abstract

PURPOSE Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B. CONCLUSION A machine learning radiomics model may help differentiate SCLC from other lung lesions.

Artificial intelligence in oncologic imaging

“…Beyond the traditional tasks of radiologists, radiomic features coupled with AI can extract and analyze quantitative data to provide tumor characterizations to guide patient treatment, including predicting histologic and molecular subtypes. For example, the ML radiomics model has shown promise in differentiating small cell lung cancer from other lung lesions on CT for pulmonary nodules at least 1 cm in size [36] . Recently, Ma et al used ML to differentiate between breast cancer molecular subtypes based on mammography and ultrasound.…”

Topological Data Analysis of Thoracic Radiographic Images shows Improved Radiomics-based Lung Tumor Histology Prediction

“…(3) The number of finite lifespans. (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) The mean, standard deviation, skewness, kurtosis, first quartile, median, third quartile, and interquartile range of the finite lifespans and finite midlifes. (20) The entropy of the finite lifespans [36].…”

“…In this paper we study the use of TDA for lung tumor histology prediction from thoracic radiographic images. Our main goal is to study the added value of TDA to all of the prominent learning problems on lung tumor CT scan images compared to state of the art quantitative imaging tools [12, 13, 14, 15, 16].…”

“…For the cancer studies, the inclusion criteria were presence of small cell lung cancer (SCLC), adenocarcinoma (ADC), or squamous cell cancer (SCC). A discussion of the inclusion, exclusion, and size criteria has been previously described [16]. The same criteria were utilized for the San Francisco VA cohort.…”

Topological data analysis of thoracic radiographic images shows improved radiomics-based lung tumor histology prediction

“…Our main goal is to study the added value of TDA to all of the prominent learning problems on lung tumor CT scan images compared with state-of-the-art quantitative imaging tools. 12 , 13 , 14 , 15 , 16 …”