Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives

The role of superior hemorrhoidal vein ectasia in the preoperative staging of rectal cancer

Objective

The prognosis of colorectal cancer has continuously improved in recent years thanks to continuous progress in both the therapeutic and diagnostic fields. The specific objective of this study is to contribute to the diagnostic field through the evaluation of the correlation between superior hemorrhoidal vein (SHV) ectasia detected on computed tomography (CT) and Tumor (T), Node (N), and distant metastasis (M) examination and mesorectal fascia (MRF) invasion in the preoperative staging of rectal cancer.

Methods

Between January 2018 and April 2022, 46 patients with histopathological diagnosis of rectal cancer were retrospectively enrolled, and the diameter of the SHV was evaluated by CT examination. The cutoff value for SHV diameter used is 3.7 mm. The diameter was measured at the level of S2 during portal venous phase after 4× image zoom to reduce the interobserver variability. The parameters evaluated were tumor location, detection of MRF infiltration (defined as the distance < 1 mm between the tumor margins and the fascia), SHV diameter, detection of mesorectal perilesional lymph nodes, and detection of metastasis.

Results

A total of 67.39% (31/46) of patients had SHV ectasia. All patients with MRF infiltration (4/46, 7.14%) presented SHV ectasia (average diameter of 4.4 mm), and SHV was significantly related with the development of liver metastases at the moment of primary staging and during follow-up.

Conclusion

SHV ectasia may be related to metastasis and MRF involvement; therefore, it could become a tool for preoperative staging of rectal cancer.

Current use of artificial intelligence in the diagnosis and management of acute appendicitis

INTRODUCTION

Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives.

EVIDENCE ACQUISITION

We performed a literature search on articles published from 2018 to September 2023. We included only original articles.

EVIDENCE SYNTHESIS

Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis.

CONCLUSIONS

AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Colorectal Cancer: Current Updates and Future Perspectives

Colorectal cancer is a frequent neoplasm in western countries, mainly due to dietary and behavioral factors. Its incidence is growing in developing countries for the westernization of foods and lifestyles. An increased incidence rate is observed in patients under 45 years of age. In recent years, the mortality for CRC is decreased, but this trend is slowing. The mortality rate is reducing in those countries where prevention and treatments have been implemented. The survival is increased to over 65%. This trend reflects earlier detection of CRC through routine clinical examinations and screening, more accurate staging through advances in imaging, improvements in surgical techniques, and advances in chemotherapy and radiation. The most important predictor of survival is the stage at diagnosis. The screening programs are able to reduce incidence and mortality rates of CRC. The aim of this paper is to provide a comprehensive overview of incidence, mortality, and survival rate for CRC.