Diagnostic value of high-resolution micro-endoscopy for the classification of colon polyps

The connection between diverticulosis and colonic superficial neoplastic lesions in patients who underwent screening colonoscopy

PURPOSE

If could be a potential pathophysiological connection between colonic diverticula and colonic superficial neoplastic lesions, beyond the shared risk factors, has been a subject of debate in the last years. This study tries to evaluate the association between diverticulosis and colonic neoplastic lesions.

METHODS

This is a cross-sectional study including asymptomatic patients who underwent a screening colonoscopy (patients with a positive fecal occult blood test under the regional program of colorectal cancer (CRC) screening), surveillance after polypectomy resection, or familiarity (first-degree relatives) between 2020 and 2021 to evaluate the association between diverticula and colonic polyps. A multivariate analysis with multiple logistic regression and odds ratio (OR) to study the independent association between adenomas and adenocarcinomas was performed.

RESULTS

One thousand five hundred one patients were included. A statistically significant association between adenomas or CRC alone and colonic diverticula was found (p = 0.045). On a multivariate analysis of demographic (age, gender) and clinical parameters (familiarity for diverticula and adenoma/CRC), only age was significantly associated with the development of colorectal adenomas or cancer (OR 1.05, 95% CI 1.03-1.07, p < 0.0001).

CONCLUSIONS

This study showed a statistically significant association between diverticula and colonic adenomas. However, it is impossible to establish a cause-effect relationship due to the intrinsic characteristics of this study design. A study with a prospective design including both patients with diverticulosis and without colonic diverticula aimed at establishing the incidence of adenoma and CRC could help to answer this relevant clinical question, since a potential association could indicate the need for closer endoscopic surveillance.

Deep learning models in medical image analysis

BACKGROUND

Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice.

HIGHLIGHT

Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry.

CONCLUSION

The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.

Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.

Rapid, High-Resolution, Label-Free, and 3-Dimensional Imaging to Differentiate Colorectal Adenomas and Non-Neoplastic Polyps With Micro-Optical Coherence Tomography

“Resect and discard” paradigm is one of the main strategies to deal with colorectal diminutive polyps after optical diagnosis. However, there are risks that unrecognized potentially malignant lesions are discarded without accurate diagnosis. The purpose of this study is to validate the potential of micro-optical coherence tomography (μOCT) to improve the diagnostic accuracy of colorectal lesions and help endoscopists make better clinical decision without additional pathology costs.