Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis

Abstract

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset “Lung-PET-CT-Dx”, obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine.

The effect of data resampling methods in radiomics

“…Another bleak explanation could be that some studies did not apply the resampling correctly. If only cross-validation is used without an independent test set, it is of utmost importance that resampling is applied only to the training set and does not utilize the validation set in any way 20 , 21 . If this is not followed, a large bias can be expected 22 , 23 ; yet, this kind of error is common 24 and often cannot be detected without access to the code, which is most often not provided in radiomic studies.…”

Current status and prospect of PET-related imaging radiomics in lung cancer

“…Dunn et al. ( 29 ) emphasized the potential of artificial intelligence-based computer-aided diagnostic tools, integrating radiomics analysis image segmentation with supervised classification, to autonomously diagnose lung cancer subtypes. Shen et al.…”

“…Zhou (28) demonstrated that 18 F-FDG PET/CT radiomics features, in conjunction with machine learning techniques, can differentiate primary from metastatic lung lesions and identify lung cancer's histological subtypes. Dunn et al (29) emphasized the potential of artificial intelligence-based computeraided diagnostic tools, integrating radiomics analysis image segmentation with supervised classification, to autonomously diagnose lung cancer subtypes. Shen et al (30) effectively classified lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by employing subregion-based radiomics features extracted from 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/ CT images of 150 ADC and 100 SCC patients.…”

Development and validation of machine-learning models for the difficulty of retroperitoneal laparoscopic adrenalectomy based on radiomics

“…Radiomics is an emerging field of medicine that combines computer science, mathematics, and medical imaging to understand better and diagnose diseases ( 13 , 14 ). Radiomics analyzes large amounts of medical imaging data to extract useful information, helping doctors make more accurate diagnoses and treatment decisions ( 15 ).…”