Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application

The pitfalls of fixed-ratio data splitting in radiomics model performance evaluation

Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like Abdominal Radiology. Despite the potential of radiomics, a major challenge lies in validating radiomics models, as most studies rely on single-center datasets with fixed-ratio splits, which can lead to variability in performance due to randomness in data splitting. Therefore, researchers should adopt more robust cross-validation methods rather than relying solely on the fixed-ratio holdout method to ensure robust and reliable radiomics model performance evaluation.

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.

Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study

The increasing use of high-resolution cross-sectional imaging has significantly enhanced the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic entities such as IPMN, MCN, and SCN. However, accurate categorization of PCLs remains a challenge. This study aims to improve PCL evaluation by developing and validating a radiomics-based software tool leveraging machine learning (ML) for lesion classification. The model categorizes PCLs into mucinous and non-mucinous types using a custom dataset of 261 CT examinations, with 156 images for training and 105 for external validation. Three experienced radiologists manually delineated the images, extracting 38 radiological and 214 radiomic features using the Pyradiomics module in Python 3.13.2. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by classification with an Adaptive Boosting (AdaBoost) model trained on the optimized feature set. The proposed model achieved an accuracy of 89.3% in the internal validation cohort and demonstrated robust performance in the external validation cohort, with 90.2% sensitivity, 80% specificity, and 88.2% overall accuracy. Comparative analysis with existing radiomics-based studies showed that the proposed model either outperforms or performs on par with the current state-of-the-art methods, particularly in external validation scenarios. These findings highlight the potential of radiomics-driven machine learning approaches in enhancing PCL diagnosis across diverse patient populations.

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact

Radiomics represents the science of extracting and analyzing a multitude of quantitative features from medical imaging, revealing the quantitative potential of radiologic images. This scientific review aims to provide radiologists with a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and prospects. The limitations of standardization in current scientific production are analyzed, along with possible solutions proposed by some of the referenced papers. As the continuous evolution of medical imaging is ongoing, radiologists must be aware of new perspectives to play a central role in patient management.

Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT

INTRODUCTION

Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims to automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis from clinical and serological data through Radiomic model based on formal methods (FMs).

METHODS

We retrospectively reviewed the CT scans acquired with Dual Source 256-slice CT scanner (Somatom Definition Flash; Siemens Healthineers, Erlangen, Germany) of 80 patients admitted to the radiology unit of Antonio Cardarelli hospital (Naples) with acute abdominal pain. Patients were divided into 2 groups: 40 underwent showed a healthy pancreatic gland, and 40 affected by four different grades (CTSI 0, 1, 2, 3) of mild pancreatitis at CT without clear clinical presentation or biochemical findings. Segmentation was manually performed. Radiologists identified 6 patients with a high expression of diseases (CTSI 3) to formulate a formal property (Rule) to detect AP in the testing set automatically. Once the rule was formulated, and Model Checker classified 70 patients into "healthy" or "unhealthy".

RESULTS

The model achieved: accuracy 81%, precision 78% and recall 81%. Combining FMs results with radiologists agreement, and applying the mode in clinical practice, the global accuracy would have been 100%.

CONCLUSIONS

Our model was reliable to automatically detect mild AP at primary diagnosis even in uncertain presentation and it will be tested prospectively in clinical practice.

Identification of hepatic metastasis from an unrevealed adenoid cystic carcinoma by PET/CT: A case report

RATIONALE

Adenoid cystic carcinoma is a rare malignant tumor of the salivary glands, with few reports of metastasis to the liver in the literature. We present a case where an isolated hepatic lesion of adenoid cystic carcinoma was identified using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT).

PATIENT CONCERNS

A 76-year-old male experienced abdominal pain and underwent an enhanced CT scan and magnetic resonance imaging, which revealed a liver mass. Subsequent 18F-FDG PET/CT identified hypermetabolic lesions in both the left and right lobes of the liver, suggesting malignancy, with no other abnormalities detected.

DIAGNOSES

A liver biopsy confirmed the diagnosis of adenoid cystic carcinoma.

INTERVENTIONS

No intervention.

OUTCOMES

Following confirmation of the diagnosis, the patient chose to discontinue treatment and was discharged.

LESSONS

Hepatic metastasis from adenoid cystic carcinoma may be detected before the identification of the primary lesion. 18F-FDG PET/CT plays a critical role in differentiating benign from malignant liver tumors, selecting potential biopsy sites, and assessing the extent of metastatic disease.

Diagnostic Anatomic Imaging for Neuroendocrine Neoplasms: Maximizing Strengths and Mitigating Weaknesses

ABSTRACT

Neuroendocrine neoplasms are a heterogeneous group of gastrointestinal and lung tumors. Their diverse clinical manifestations, variable locations, and heterogeneity present notable diagnostic challenges. This article delves into the imaging modalities vital for their detection and characterization. Computed tomography is essential for initial assessment and staging. At the same time, magnetic resonance imaging (MRI) is particularly adept for liver, pancreatic, osseous, and rectal imaging, offering superior soft tissue contrast. The article also highlights the limitations of these imaging techniques, such as MRI's inability to effectively evaluate the cortical bone and the questioned cost-effectiveness of computed tomography and MRI for detecting specific gastric lesions. By emphasizing the strengths and weaknesses of these imaging techniques, the review offers insights into optimizing their utilization for improved diagnosis, staging, and therapeutic management of neuroendocrine neoplasms.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma

BACKGROUND

One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.

AIM

To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC.

METHODS

A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model.

RESULTS

A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.

CONCLUSION

The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN)

Intraductal papillary mucinous neoplasms (IPMNs) are a very common incidental finding during patient radiological assessment. These lesions may progress from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and even pancreatic cancer. The IPMN progression risk grows with time, so discontinuation of surveillance is not recommended. It is very important to identify imaging features that suggest LGD of IPMNs, and thus, distinguish lesions that only require careful surveillance from those that need surgical resection. It is important to know the management guidelines and especially the indications for surgery, to be able to point out in the report the findings that suggest malignant degeneration. The imaging tools employed for diagnosis and risk assessment are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) with contrast medium. According to the latest European guidelines, MRI is the method of choice for the diagnosis and follow-up of patients with IPMN since this tool has a highest sensitivity in detecting mural nodules and intra-cystic septa. It plays a key role in the diagnosis of worrisome features and high-risk stigmata, which are associated with IPMNs malignant degeneration. Nowadays, the main limit of diagnostic tools is the ability to identify the precursor of pancreatic cancer. In this context, increasing attention is being given to artificial intelligence (AI) and radiomics analysis. However, these tools remain in an exploratory phase, considering the limitations of currently published studies. Key limits include noncompliance with AI best practices, radiomics workflow standardization, and clear reporting of study methodology, including segmentation and data balancing. In the radiological report it is useful to note the type of IPMN so as the morphological features, size, rate growth, wall, septa and mural nodules, on which the indications for surveillance and surgery are based. These features should be reported so as the surveillance time should be suggested according to guidelines.