Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets

Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images

Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carcinoma (HCC) and metastases. It is crucial for the management and prevention strategies to accurately differentiate between these two liver cancers. This study proposes an automated system using a convolutional neural network (CNN) to enhance diagnostic accuracy to detect HCC, metastasis, and healthy liver tissue. This system incorporates automatic segmentation and classification. The liver lesions segmentation model is implemented using residual attention U-Net. A 9-layer CNN classifier implements the lesions classification model. Its input is the combination of the results of the segmentation model with original images. The dataset included 300 patients, with 223 used to develop the segmentation model and 77 to test it. These 77 patients also served as inputs for the classification model, consisting of 20 HCC cases, 27 with metastasis, and 30 healthy. The system achieved a mean Dice score of 87.65 % in segmentation and a mean accuracy of 93.97 % in classification, both in the test phase. The proposed method is a preliminary study with great potential in helping radiologists diagnose liver cancers.

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis : Neural Network-based Methods for Liver Semantic Segmentation

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.

Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature

BACKGROUND AND AIM

Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis.

METHODS

A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information.

RESULTS

Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970.

CONCLUSIONS

AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

РОЛЬ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ

Hepatosellülyar karsinoma (HSK) ən çox yayılan bədxassəli törəmələr arasında beşinci yeri tutur və dünyada xərçənglə əlaqəli ölümün üçüncü ən çox yayılmış səbəbidir. Süni intellekt (Sİ) sürətlə artan maraq sahəsidir. Müəlliflər HSK-ın diaqnostikasında və qiymətləndirilməsində Sİ-nin tətbiqi barədə məlumat verən məqalələri araşdırmışlar. Bu məqsədlə 27 məqalə təhlil edilmişdir. Təhlil edilmiş məqalələrdən KT görüntülərinin tədqiqinə dair 19 məqalədə (41,30%), USQ görüntülərinin öyrənilməsini əks etdirən 20 (43,47%) və MRT görüntülərindən bəhs edən 7 məqalədə (15,21%) müxtəlif Sİ alqoritmləri qəbul edilmişdir. Heç bir məqalədə PET və rentgen texnologiyasında süni intellektin istifadəsi müzakirə edilməyib. Sistematik yanaşma göstərmişdir ki, HSK-nin diaqnostikası və qiymətləndirilməsi üzrə əvvəlki işlərdə USQ, KT və MRT istifadə edilərək ənənəvi şərhin maşın öyrənməsi ilə müqayisəliliyi qiymətləndirilmişdir. Təhlillərimizdə görüntüləmə üsullarının istifadəsi HSK diaqnostikası üçün tibbi görüntüləmənin faydalılığını və təkamülünü əks etdirir. Bundan əlavə, nəticələrimiz lazımsız təkrarlanmanı və resursların israfını minimuma endirmək üçün birgə məlumat bazasında məlumat mübadiləsinə qaçılmaz ehtiyac olduğunu vurğulayır.

Гепатоцеллюлярная карцинома является пятым по распространенности злокачественным новообразованием и третьей по частоте причиной смерти от рака во всём мире. Искусственный интеллект — это быстрорастущая область интересов. Авторами были рассмотрены статьи, в которых сообщается о применении алгоритмов ИИ в диагностике и оценке ГЦК. Для этого проанализированы 27 статей. В проанализированных статьях в 19 статьях, посвящённых КТ-изображениям (41,30%), в 20 статьях, посвящённых изображениям УЗИ (43,47%), и в 7 статьях, посвящённым МРТ-изображениям (15,21%), использовали разные алгоритмы ИИ. Ни в одной статье не обсуждалось использование искусственного интеллекта в ПЭТ и рентгеновские технологии. Системный подход показал, что предыдущая работа по диагностике и оценке ГЦК оценивала сопоставимость традиционной интерпретации с машинным обучением с использованием УЗИ, КТ и МРТ. Использование методов визуализации в проведенном анализе отражает полезность и эволюцию медицинской визуализации для диагностики ГЦК. Кроме того, результаты поиска литературы подчёркивают острую необходимость совместного использования данных в совместных базах данных, чтобы свести к минимуму ненужное дублирование и растрату ресурсов.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer death worldwide. Artificial intelligence (AI) is a rapidly growing area of interest. We have reviewed articles reporting the application of AI algorithms in the diagnosis and evaluation of HCC. To do this, we analyzed 27 articles. In the analyzed articles, 19 articles on CT images (41.30%), 20 articles on ultrasound images (43.47%), and 7 articles on MRI images (15.21%) used different AI algorithms. None of the articles discussed the use of artificial intelligence in PET and X-ray technologies. Our systematic approach showed that previous work on the diagnosis and evaluation of HCC assessed the comparability of traditional interpretation with machine learning using ultrasound, CT, and MRI. The use of imaging modalities in our analysis reflects the usefulness and evolution of medical imaging for diagnosing HCC. In addition, our results highlight the critical need to share data across collaborative databases to minimize unnecessary duplication and waste of resources.

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary liver cancer. Hundreds of studies involving thousands of patients have now been analysed across different cancer types, including HCC, regarding the effects of immune infiltrates on the prognosis of cancer patients. However, for these analyses, an unambiguous delineation of the cancer area is paramount, which is difficult due to the strong heterogeneity and considerable inter-operator variability induced by qualitative visual assessment and manual assignment. Nowadays, however, multiplex analyses allow the simultaneous evaluation of multiple protein markers, which, in conjunction with recent machine learning approaches, may offer great potential for the objective, enhanced identification of cancer areas with further in situ analysis of prognostic immune parameters. In this study, we, therefore, used an exemplary five-marker multiplex immunofluorescence panel of commonly studied markers for prognosis (CD3 T, CD4 T helper, CD8 cytotoxic T, FoxP3 regulatory T, and PD-L1) and DAPI to assess which analytical approach is best suited to combine morphological and immunohistochemical data into a cancer score to identify the cancer area that best matches an independent pathologist’s assignment. For each cell, a total of 68 individual cell features were determined, which were used as input for 4 different approaches for computing a cancer score: a correlation-based selection of individual cell features, a MANOVA-based selection of features, a multilayer perceptron, and a convolutional neural network (a U-net). Accuracy was used to evaluate performance. With a mean accuracy of 75%, the U-net was best capable of identifying the cancer area. Although individual cell features showed a strong heterogeneity between patients, the spatial representations obtained with the computed cancer scores delineate HCC well from non-cancer liver tissues. Future analyses with larger sample sizes will help to improve the model and enable direct, in-depth investigations of prognostic parameters, ultimately enabling precision medicine.

Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.

Imaging Diagnosis of HCC: Future directions with special emphasis on hepatobiliary MRI and contrast-enhanced ultrasound

Hepatocellular carcinoma (HCC) is a unique cancer entity that can be noninvasively diagnosed using imaging modalities without pathologic confirmation. In 2018, several major guidelines for HCC were updated to include hepatobiliary contrast agent magnetic resonance imaging (HBA-MRI) and contrast-enhanced ultrasound (CEUS) as major imaging modalities for HCC diagnosis. HBA-MRI enables the achievement of high sensitivity in HCC detection using the hepatobiliary phase (HBP). CEUS is another imaging modality with real-time imaging capability, and it is reported to be useful as a second-line modality to increase sensitivity without losing specificity for HCC diagnosis. However, until now, there is an unsolved discrepancy among guidelines on whether to accept “HBP hypointensity” as a definite diagnostic criterion for HCC or include CEUS in the diagnostic algorithm for HCC diagnosis. Furthermore, there is variability in terminology and inconsistencies in the definition of imaging findings among guidelines; therefore, there is an unmet need for the development of a standardized lexicon. In this article, we review the performance and limitations of HBA-MRI and CEUS after guideline updates in 2018 and briefly introduce some future aspects of imaging-based HCC diagnosis.