Focal liver lesion diagnosis with deep learning and multistage CT imaging

Applications of artificial intelligence in abdominal imaging

The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challenges in disease detection, classification, and personalized care. AI applications, particularly those leveraging deep learning and radiomics, have demonstrated remarkable accuracy in detecting a wide range of abdominal conditions, including but not limited to diffuse liver parenchymal disease, focal liver lesions, pancreatic ductal adenocarcinoma (PDAC), renal tumors, and bowel pathologies. These models excel in the automation of tasks such as segmentation, classification, and prognostication across modalities like ultrasound, CT, and MRI, often surpassing traditional diagnostic methods. Despite these advancements, widespread adoption remains limited by challenges such as data heterogeneity, lack of multicenter validation, reliance on retrospective single-center studies, and the "black box" nature of many AI models, which hinder interpretability and clinician trust. The absence of standardized imaging protocols and reference gold standards further complicates integration into clinical workflows. To address these barriers, future directions emphasize collaborative multi-center efforts to generate diverse, standardized datasets, integration of explainable AI frameworks to existing picture archiving and communication systems, and the development of automated, end-to-end pipelines capable of processing multi-source data. Targeted clinical applications, such as early detection of PDAC, improved segmentation of renal tumors, and improved risk stratification in liver diseases, show potential to refine diagnostic accuracy and therapeutic planning. Ethical considerations, such as data privacy, regulatory compliance, and interdisciplinary collaboration, are essential for successful translation into clinical practice. AI's transformative potential in abdominal imaging lies not only in complementing radiologists but also in fostering precision medicine by enabling faster, more accurate, and patient-centered care. Overcoming current limitations through innovation and collaboration will be pivotal in realizing AI's full potential to improve patient outcomes and redefine the landscape of abdominal radiology.

Deep Learning to Enhance Diagnosis and Management of Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is associated with a poor prognosis and necessitates a multimodal, multidisciplinary approach from diagnosis to treatment to achieve optimal outcomes. A noninvasive preoperative diagnosis using abdominal imaging techniques can represent a clinical challenge. Given the differential response of iCCA to localized and systemic therapies compared with hepatocellular carcinoma and secondary hepatic malignancies, an accurate diagnosis is crucial. Deep learning (DL) models for image analysis have emerged as a promising adjunct for the abdominal radiologist, potentially enhancing the accurate detection and diagnosis of iCCA. Over the last five years, several reports have proposed robust DL models, which demonstrate a diagnostic accuracy that is either comparable to or surpasses that of radiologists with varying levels of experience. Recent studies have expanded DL applications into other aspects of iCCA management, including histopathologic diagnosis, the prediction of histopathological features, the preoperative prediction of survival, and the pretreatment prediction of responses to systemic therapy. We herein critically evaluate the expanding body of research on DL applications in the diagnosis and management of iCCA, providing insights into the current progress and future research directions. We comprehensively synthesize the performance and limitations of DL models in iCCA research, identifying key challenges that serve as a translational reference for clinicians.

Delaying liver aging: Analysis of structural and functional alterations

This article is based on a recent bibliometric analysis of research progress on liver aging. The liver is notable for its extraordinary ability to rejuvenate, thereby safeguarding and maintaining the organism's integrity. With advancing age, there is a noteworthy reduction in both the liver's size and blood circulation. Furthermore, the wide range of physiological alterations driven on by aging may foster the development of illnesses. Previous studies indicate that liver aging is linked to impaired lipid metabolism and abnormal gene expression associated with chronic inflammation. Factors such as mitochondrial dysfunction and telomere shortening accumulate, which may result in increased hepatic steatosis, which impacts liver regeneration, metabolism, and other functions. Knowing the structural and functional changes could help elderly adults delay liver aging. Increasing public awareness of anti-aging interventions is essential. Besides the use of dietary supplements, alterations in lifestyle, including changes in dietary habits and physical exercise routines, are the most efficacious means to decelerate the aging process of the liver. This article highlights recent advances in the mechanism research of liver aging and summarizes the promising intervention options to delay liver aging for preventing related diseases.

Efficacy of quantitative parameters of three-phase contrast-enhanced computed tomography combined with serum miR-122 and miR-224 in diagnosis of liver space-occupying lesions with fatty liver

BACKGROUND

Three-phase contrast-enhanced computed tomography (CT) is a commonly used diagnostic method for liver space-occupying lesions, and microRNA is an important biomarker that can regulate cell proliferation and apoptosis. The condition of liver space-occupying lesions with fatty liver is complex, so we explored the diagnostic value of combining quantitative parameters of three-phase contrast-enhanced CT with serum microRNA-122 and microRNA-224 for liver space-occupying lesions with fatty liver.

AIM

To explore the efficacy of contrast-enhanced CT scanning combined with serum miRNA-122 (miR-122) and miRNA-224 (miR-224) in the diagnosis of liver space-occupying lesions with fatty liver, to provide a reference for their clinical diagnosis and treatment.

METHODS

A prospective study was conducted on 80 patients with liver space-occupying lesions accompanied by fatty liver who were admitted to the Chun'an County Traditional Chinese Medicine Hospital from June 2021 to June 2024. The surgical or liver biopsy pathology results were used as the "gold standard" to determine the benign or malignant nature of the liver space-occupying lesions accompanied by fatty liver. Before pathological examination, all patients underwent contrast-enhanced CT scanning and the levels of serum miR-122 and miR-224 were measured. The CT values, enhancement indexes, and serum levels of miR-122 and miR-224 were compared in patients with different types of liver space-occupying lesions accompanied by fatty liver, and the efficacy of three-phase contrast-enhanced CT combined with serum levels of miR-122 and miR-224 in diagnosing malignant liver space-occupying lesions accompanied by fatty liver was evaluated.

RESULTS

Among the 80 patients with liver space-occupying lesions accompanied by fatty liver, 46 had benign lesions and 34 had malignant lesions. On contrast-enhanced CT, malignant lesions exhibited a higher probability of presence of capsules and fast-in and fast-out pattern compared to benign lesions. Additionally, the probability of uniform density and central scarring was lower in malignant lesions than in benign lesions (P < 0.05). The CT values and enhancement indexes of malignant lesions in the arterial phase, portal venous phase, and delayed phase were lower than those of benign lesions (P < 0.05). The serum level of miR-122 in malignant lesions with fatty liver was lower than that in benign lesions, while the level of miR-224 was higher than that in benign lesions (P < 0.05). The receiver operating characteristic curves of enhanced CT quantitative parameters, serum miR-122, and miR-224, alone or in combination, for the diagnosis of malignant lesions with fatty liver were drawn. The results showed that the area under curve of the combination of enhanced CT quantitative parameters, miR-122, and miR-224 was the largest at 0.946 (95% confidence interval: 0.972-0.984) (P < 0.05).

CONCLUSION

There are significant differences in quantitative parameters of contrast-enhanced CT between patients with benign and malignant liver space-occupying lesions with fatty liver. The combination of enhanced CT parameters and serum miR-122 and miR-224 has high diagnostic efficiency for malignant liver space-occupying lesions with fatty liver.

A deep learning-based psi CT network effectively predicts early recurrence after hepatectomy in HCC patients

BACKGROUND

Hepatocellular carcinoma (HCC) exhibits a high recurrence rate, and early recurrence significantly jeopardizes patient prognosis, necessitating reliable methods for early recurrence prediction.

METHODS

Utilizing multi-institutional data and integrating deep learning (DL) techniques, we established a neural network based on DenseNet capable of concurrently processing patients' triphasic enhanced CT scans. By incorporating an attention mechanism, the model automatically focuses on regions that significantly impact patient survival. Performance metrics were first evaluated using the concordance index (C-index), calibration curves, and decision curves based on the training and validation cohorts. Finally, class activation map (CAM) techniques were employed to visualize the regions of interest identified by the model. After model construction, five-fold cross-validation was performed to assess overfitting risks and further evaluate model stability.

RESULTS

We retrospectively collected data from 302 cases across five centers, including patients who underwent Partial Hepatectomy between December 2016 and December 2022. During model development, 180 patients from Institution I formed the training cohort, while the remaining patients comprised the validation cohort. The area under the ROC curve (AUC) for two-year outcomes was 0.797 in the validation cohort. Calibration curves, survival curves, and decision curve analysis (DCA) demonstrated the model's robust performance. CAMs revealed that the model primarily focuses on intra-abdominal solid organs, consistent with clinical experience. After model development, datasets were merged for cross-validation. The best model achieved a C-index of 0.774 in the validation cohort, with five-fold cross-validation yielding an average C-index of 0.778. The 95% confidence interval (CI) for the C-index, derived from cross-validation, ranged from 0.762 to 0.793.

CONCLUSION

Our DL-based enhanced CT network shows promise in predicting early recurrence in patients, representing a potential new strategy for early recurrence prediction in HCC.

Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review

Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models.