Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study

BACKGROUND. Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. OBJECTIVE. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on routine abdominopelvic CT examinations performed without bowel preparation. METHODS. This retrospective study included 3945 patients (2275 men, 1670 women; mean age, 62 years): a training set of 2662 patients from Severance Hospital with CRC who underwent routine contrast-enhanced abdominopelvic CT before treatment between January 2010 and December 2014 and internal (841 patients from Severance Hospital) and external (442 patients from Gangnam Severance Hospital) test sets of patients who underwent routine contrast-enhanced abdominopelvic CT for any indication and colonoscopy within a 2-month interval between January 2018 and June 2018. A radiologist, accessing colonoscopy reports, determined which CRCs were visible on CT and placed bounding boxes around lesions on all slices showing CRC, serving as the reference standard. A contemporary transformer-based object detection network was adapted and trained to create an AI model (https://github.com/boktae7/colorectaltumor) to automatically detect CT-visible CRC on unprocessed DICOM slices. AI performance was evaluated using alternative free-response ROC analysis, per-lesion sensitivity, and per-patient specificity; performance in the external test set was compared with that of two radiologist readers. Clinical radiology reports were also reviewed. RESULTS. In the internal (93 CT-visible CRCs in 92 patients) and external (26 CT-visible CRCs in 26 patients) test sets, AI had AUC of 0.867 and 0.808, sensitivity of 79.6% and 80.8%, and specificity of 91.2% and 90.9%, respectively. In the external test set, the two radiologists had sensitivities of 73.1% and 80.8% (p = .74 and p > .99 vs AI) and specificities of 98.3% and 98.6% (both p < .001 vs AI); AI correctly detected five of nine CRCs missed by at least one reader. The clinical radiology reports raised suspicion for 75.9% of CRCs in the external test set. CONCLUSION. The findings show the AI model's utility for automated detection of CRC on routine abdominopelvic CT examinations. CLINICAL IMPACT. The AI model could help reduce the frequency of missed CRCs on routine examinations performed for reasons unrelated to CRC detection.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

N-Net: Lesion region segmentations using the generalized hybrid dilated convolutions for polyps in colonoscopy images

Colorectal cancer is the cancer with the second highest and the third highest incidence rates for the female and the male, respectively. Colorectal polyps are potential prognostic indicators of colorectal cancer, and colonoscopy is the gold standard for the biopsy and the removal of colorectal polyps. In this scenario, one of the main concerns is to ensure the accuracy of lesion region identifications. However, the missing rate of polyps through manual observations in colonoscopy can reach 14%–30%. In this paper, we focus on the identifications of polyps in clinical colonoscopy images and propose a new N-shaped deep neural network (N-Net) structure to conduct the lesion region segmentations. The encoder-decoder framework is adopted in the N-Net structure and the DenseNet modules are implemented in the encoding path of the network. Moreover, we innovatively propose the strategy to design the generalized hybrid dilated convolution (GHDC), which enables flexible dilated rates and convolutional kernel sizes, to facilitate the transmission of the multi-scale information with the respective fields expanded. Based on the strategy of GHDC designing, we design four GHDC blocks to connect the encoding and the decoding paths. Through the experiments on two publicly available datasets on polyp segmentations of colonoscopy images: the Kvasir-SEG dataset and the CVC-ClinicDB dataset, the rationality and superiority of the proposed GHDC blocks and the proposed N-Net are verified. Through the comparative studies with the state-of-the-art methods, such as TransU-Net, DeepLabV3+ and CA-Net, we show that even with a small amount of network parameters, the N-Net outperforms with the Dice of 94.45%, the average symmetric surface distance (ASSD) of 0.38 pix and the mean intersection-over-union (mIoU) of 89.80% on the Kvasir-SEG dataset, and with the Dice of 97.03%, the ASSD of 0.16 pix and the mIoU of 94.35% on the CVC-ClinicDB dataset.

Vector textures derived from higher order derivative domains for classification of colorectal polyps

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves.

Necrosis volume and Choi criteria predict the response to endoscopic ultrasonography-guided HybridTherm ablation of locally advanced pancreatic cancer

Background and study aims  Endoscopic ultrasound (EUS)-guided ablation of pancreatic ductal adenocarcinoma (PDAC) with HybridTherm-Probe (EUS-HTP) is feasible and safe, but the radiological response and ideal tool to measure it have not been investigated yet. The aims of this study were to: 1) assess the radiological response to EUS-HTP evaluating the vital tumor volume reduction rate, Response Evaluation Criteria in Solid Tumors (RECIST1.1) and Choi criteria; 2) determine the prognostic predictive yield of these criteria. Patients and methods  A retrospective analysis was performed of patients with locally advanced PDAC after primary treatment or unfit for chemotherapy prospectively treated by EUS-HTP. Computed tomography scan was performed 1 month after EUS-HTP to evaluate: 1) vital tumor volume reduction rate (VTVRR) by measuring necrosis and tumor volumes through a computer-aided detection system; and 2) RECIST1.1 and Choi criteria. Results  EUS-HTP was feasible in 22 of 31 patients (71 %), with no severe adverse events. Median post-HTP survival was 7 months (1 - 35). Compared to pre-HTP tumor volume, a significant 1-month VTVRR (mean 21.4 %) was observed after EUS-HTP ( P  = 0.005). We identified through ROC analysis a VTVRR > 11.46 % as the best cut-off to determine post-HTP 6-month survival outcome (AUC = 0.733; sensitivity = 70.0 %, specificity = 83.3 %). This cut-off was significantly associated with longer overall survival (HR = 0.372; P  = 0.039). According to RECIST1.1 and Choi criteria, good responders to EUS-HTP were 60 % and 46.7 %, respectively. Good responders according to Choi, but not to RECIST1.1, had longer survival (HR = 0.407; P  = 0.04). Conclusions  EUS-HTP induces a significant 1-month VTVRR. This effect is assessed accurately by evaluation of necrosis and tumor volumes. Use of VTVRR and Choi criteria, but not RECIST 1.1 criteria, might identify patients who could benefit clinically from EUS-HTP.

A review of medical image detection for cancers in digestive system based on artificial intelligence

Introduction: At present, cancer imaging examination relies mainly on manual reading of doctors, which requests a high standard of doctors' professional skills, clinical experience, and concentration. However, the increasing amount of medical imaging data has brought more and more challenges to radiologists. The detection of digestive system cancer (DSC) based on artificial intelligence (AI) can provide a solution for automatic analysis of medical images and assist doctors to achieve high-precision intelligent diagnosis of cancers. Areas covered: The main goal of this paper is to introduce the main research methods of the AI based detection of DSC, and provide relevant reference for researchers. Meantime, it summarizes the main problems existing in these methods, and provides better guidance for future research. Expert commentary: The automatic classification, recognition, and segmentation of DSC can be better realized through the methods of machine learning and deep learning, which minimize the internal information of images that are difficult for humans to discover. In the diagnosis of DSC, the use of AI to assist imaging surgeons can achieve cancer detection rapidly and effectively and save doctors' diagnosis time. These can lay the foundation for better clinical diagnosis, treatment planning and accurate quantitative evaluation of DSC.

Machine learning for computer-aided polyp detection using wavelets and content-based image

The continuous growing of machine learning techniques, their capabilities improvements and the availability of data being continuously collected, recorded and updated, can enhance diagnosis stages by making it faster and more accurate than human diagnosis. In lower endoscopies procedures, most of the diagnosis relies on the capabilities and expertise of the physician. During medical training, physicians can be benefited from the assistance of algorithms able to automatically detect polyps, thus enhancing their diagnosis. In this paper, we propose a machine learning approach trained to detect polyps in lower endoscopies recordings with high accuracy and sensitivity, previously processed using wavelet transform for feature extraction. The propose system is validated using available datasets. From a set of 1132 images, our system showed a 97.9% of accuracy in diagnosing polyps, around 10% more efficient than other approaches using techniques with a low computational requirement previously published. In addition, the false positive rate was 0.03. This encouraging result can be also extended to other diagnosis.