Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images

Artificial intelligence for detection and characterization of focal hepatic lesions: a review

Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be malignant or pre-malignant, and need accurate detection and classification. Current imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), play a crucial role in assessing these lesions. Artificial intelligence (AI), particularly deep learning (DL), offers potential solutions by analyzing large data to identify patterns and extract clinical features that aid in the early detection and classification of FLLs. This manuscript reviews the diagnostic capacity of AI-based algorithms in processing CT and MRIs to detect benign and malignant FLLs, with an emphasis in the characterization and classification of these lesions and focusing on differentiating benign from pre-malignant and potentially malignant lesions. A comprehensive literature search from January 2010 to April 2024 identified 45 relevant studies. The majority of AI systems employed convolutional neural networks (CNNs), with expert radiologists providing reference standards through manual lesion delineation, and histology as the gold standard. The studies reviewed indicate that AI-based algorithms demonstrate high accuracy, sensitivity, specificity, and AUCs in detecting and characterizing FLLs. These algorithms excel in differentiating between benign and malignant lesions, optimizing diagnostic protocols, and reducing the needs of invasive procedures. Future research should concentrate on the expansion of data sets, the improvement of model explainability, and the validation of AI tools across a range of clinical setting to ensure the applicability and reliability of such tools.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma.

METHODS

The PubMed database was searched for papers published in the English language no earlier than October 2022.

RESULTS

We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective.

CONCLUSIONS

It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Purpose

To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model.

Methods

Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared.

Results

Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2.

Conclusion

The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03183-9.

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

BACKGROUND

Cholangiocarcinoma (CCA) is a relatively rare malignant biliary system tumor, and yet it represents the second most common primary hepatic neoplasm, following hepatocellular carcinoma. Regardless of the type, location, or etiology, the survival prognosis of these tumors remains poor. The only method of cure for CCA is complete surgical resection, but part of patients with complete resection are still subject to local recurrence or distant metastasis.

SUMMARY

Over the last several decades, our understanding of the molecular biology of CCA has increased tremendously, diagnostic and evaluative techniques have evolved, and novel therapeutic approaches have been established. Key Messages: This review provides an overview of preoperative imaging evaluations of CCA. Furthermore, relevant information about artificial intelligence (AI) in medical imaging is discussed, as well as the development of AI in CCA treatment.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer: frequency and prediction

PURPOSE

To determine clinical factors for predicting the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer.

METHODS AND MATERIALS

The study group consisted of 109 patients with esophageal cancer of T1-4 and Stage I-III who were treated with definitive radiotherapy and achieved a complete response of their primary lesion at Kyushu University Hospital between January 1998 and December 2007. Esophageal stenosis was evaluated using esophagographic images within 3 months after completion of radiotherapy. We investigated the correlation between esophageal stenosis after radiotherapy and each of the clinical factors with regard to tumors and therapy. For validation of the correlative factors for esophageal stenosis, an artificial neural network was used to predict the esophageal stenotic ratio.

RESULTS

Esophageal stenosis tended to be more severe and more frequent in T3-4 cases than in T1-2 cases. Esophageal stenosis in cases with full circumference involvement tended to be more severe and more frequent than that in cases without full circumference involvement. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. In the multivariate analysis, T stage, extent of involved circumference, and wall thickness of the tumor region were significantly correlated to esophageal stenosis (p = 0.031, p < 0.0001, and p = 0.0011, respectively). The esophageal stenotic ratio predicted by the artificial neural network, which learned these three factors, was significantly correlated to the actual observed stenotic ratio, with a correlation coefficient of 0.864 (p < 0.001).

CONCLUSION

Our study suggested that T stage, extent of involved circumference, and esophageal wall thickness of the tumor region were useful to predict the frequency and severity of esophageal stenosis associated with tumor regression in radiotherapy for esophageal cancer.

Differentiation of common large sellar-suprasellar masses effect of artificial neural network on radiologists' diagnosis performance

RATIONALE AND OBJECTIVES

When pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst grow in the sellar and suprasellar region, it is often difficult to differentiate among these three lesions on magnetic resonance (MR) images. The purpose of this study was to apply an artificial neural network (ANN) for differential diagnosis among these three lesions with MR images and retrospectively evaluate the effect of ANN output on radiologists' performance.

MATERIALS AND METHODS

Forty-three patients with sellar-suprasellar masses were studied. The ANN was designed to differentiate among pituitary adenoma, craniopharyngioma, and Rathke's cleft cyst by using patients' ages and nine MR image findings obtained by three neuroradiologists using a subjective rating scale. In the observer performance test, MR images were viewed by nine radiologists, including four neuroradiologists and five general radiologists, first without and then with ANN output. The radiologists' performance was evaluated using receiver-operating characteristic analysis with a continuous rating scale.

RESULTS

The ANN showed high performance in differentiation among the three lesions (area under the receiver-operating characteristic curve, 0.990). The average area under the curve for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = .0024) when they used the computer output. Areas under the curves for the general radiologists and neuroradiologists increased from 0.876 to 0.983 (P = .0083) and from 0.952 to 0.989 (P = .038), respectively.

CONCLUSION

In diagnostic performance for differentiation among pituitary macroadenoma, craniopharyngioma, and Rathke's cleft cyst with MR imaging, the ANN resulted in parity between neuroradiologists and general radiologists.

Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images

BACKGROUND AND PURPOSE

Previous studies have suggested that use of an artificial neural network (ANN) system is beneficial for radiological diagnosis. Our purposes in this study were to construct an ANN for the differential diagnosis of intra-axial cerebral tumors on MR images and to evaluate the effect of ANN outputs on radiologists' diagnostic performance.

MATERIALS AND METHODS

We collected MR images of 126 patients with intra-axial cerebral tumors (58 high-grade gliomas, 37 low-grade gliomas, 19 metastatic tumors, and 12 malignant lymphomas). We constructed a single 3-layer feed-forward ANN with a Levenberg-Marquardt algorithm. The ANN was designed to differentiate among 4 categories of tumors (high-grade gliomas, low-grade gliomas, metastases, and malignant lymphomas) with use of 2 clinical parameters and 13 radiologic findings in MR images. Subjective ratings for the 13 radiologic findings were provided independently by 2 attending radiologists. All 126 cases were used for training and testing of the ANN based on a leave-one-out-by-case method. In the observer test, MR images were viewed by 9 radiologists, first without and then with ANN outputs. Each radiologist's performance was evaluated through a receiver operating characteristic (ROC) analysis on a continuous rating scale.

RESULTS

The averaged area under the ROC curve for ANN alone was 0.949. The diagnostic performance of the 9 radiologists increased from 0.899 to 0.946 (P < .001) when they used ANN outputs.

CONCLUSIONS

The ANN can provide useful output as a second opinion to improve radiologists' diagnostic performance in the differential diagnosis of intra-axial cerebral tumors seen on MR imaging.

Cholangiocarcinoma: emerging approaches to a challenging cancer

PURPOSE OF REVIEW

Cholangiocarcinomas are malignancies arising from biliary tract epithelia that are associated with a poor prognosis. The diagnosis, evaluation and management of these tumors continue to be clinically challenging. In this review, we will discuss recent information that may influence the approach to the diagnosis and management of this devastating tumor.

RECENT FINDINGS

Epidemiological studies are defining patient groups at increased risk of cancer in regions of low prevalence. Emerging approaches that may improve evaluation and management include the potential use of genomic and proteomic markers, cytogenetic and digital image analysis, endoscopic ultrasound with fine needle aspiration, and cholangioscopy. Adoption of aggressive surgical approaches along with the use of preoperative portal vein embolization, hepatic and caudate lobe resection and preoperative laparoscopy may improve survival.

SUMMARY

Recent conceptual and technical advances will impact the clinical approach to cholangiocarcinoma and offer the promise of improving outcomes from this difficult tumor.