Machine Learning–based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study

Grosu, Sergio; Wesp, Philipp; Graser, Anno; Maurus, Stefan; Schulz, Christian; Knösel, Thomas; Cyran, Clemens C.; Ricke, Jens; Ingrisch, Michael; Kazmierczak, Philipp M.

doi:10.1148/radiol.2021202363

Cited by 47 publications

(30 citation statements)

References 39 publications

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“…The results of this research revealed that the area under the receiver operating characteristic (ROC) curve (AUC) of classification in distinguishing colorectal lesions (neoplastic and non-neoplastic) was improved from 0.74 (by using the image intensity solely) to 0.85 (by also using the texture features from high-order differentiations). In another study, Grosu et al [52] developed a machine learning method to distinguish between benign and precancerous CTC-detected colorectal polyps in an average-risk asymptomatic CRC screening sample. The current classification algorithm showed promising results with a sensitivity of 82%, a specificity of 85%, and an AUC of 0.91.…”

Section: Virtual Colonoscopymentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

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Section: Virtual Colonoscopymentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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“…The entire code we used during the model establishment is publicly available on the development platform Github ( ) ( 25 ). After 142 lesions were divided into LGG or GBM based on histopathological examination, Random Forest classifier was used to construct a radiomics model.…”

Section: Methodsmentioning

confidence: 99%

Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas in The Cancer Imaging Archive Database: A Proof-of-Concept Study

Zhang

Pan

et al. 2022

Front. Oncol.

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PurposeThis study aimed to develop a repeatable MRI-based machine learning model to differentiate between low-grade gliomas (LGGs) and glioblastoma (GBM) and provide more clinical information to improve treatment decision-making.MethodsPreoperative MRIs of gliomas from The Cancer Imaging Archive (TCIA)–GBM/LGG database were selected. The tumor on contrast-enhanced MRI was segmented. Quantitative image features were extracted from the segmentations. A random forest classification algorithm was used to establish a model in the training set. In the test phase, a random forest model was tested using an external test set. Three radiologists reviewed the images for the external test set. The area under the receiver operating characteristic curve (AUC) was calculated. The AUCs of the radiomics model and radiologists were compared.ResultsThe random forest model was fitted using a training set consisting of 142 patients [mean age, 52 years ± 16 (standard deviation); 78 men] comprising 88 cases of GBM. The external test set included 25 patients (14 with GBM). Random forest analysis yielded an AUC of 1.00 [95% confidence interval (CI): 0.86–1.00]. The AUCs for the three readers were 0.92 (95% CI 0.74–0.99), 0.70 (95% CI 0.49–0.87), and 0.59 (95% CI 0.38–0.78). Statistical differences were only found between AUC and Reader 1 (1.00 vs. 0.92, respectively; p = 0.16).ConclusionAn MRI radiomics-based random forest model was proven useful in differentiating GBM from LGG and showed better diagnostic performance than that of two inexperienced radiologists.

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“…Histopathological polyp class was blinded for all readers. Colorectal polyps were manually segmented in multiplanar 2D CT colonography images by a board-certified radiologist (8 years of experience in CT colonography imaging; completed a specialised hands-on workshop on CT colonography) and two radiology residents (3 years of experience in CT colonography imaging; one completed a specialised hands-on workshop on CT colonography) in equal amounts, as described in detail beforehand [16]. For exact retrospective polyp re-detection, 2D and virtual fly-through 3D CT colonography reconstructions were used (Fig.…”

Section: Polyp Segmentationmentioning

confidence: 99%

“…First studies have shown that machine learning-based CT colonography using radiomics may allow non-invasive differentiation of benign and premalignant colorectal polyps [15,16]. These radiomics approaches consist of three steps.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps

Wesp

Grosu

Graser³

et al. 2022

Eur Radiol

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Objectives To investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning. Methods In this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++. Results The training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue. Conclusions In this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader. Key Points • Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans. • Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts. • Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6–9 mm size.

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Machine Learning–based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study

Cited by 47 publications

References 39 publications

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas in The Cancer Imaging Archive Database: A Proof-of-Concept Study

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps

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