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The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.
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Sarcopenia, an extremely common condition in cancer patients, is described as a progressive and generalized musculoskeletal disorder that is associated with an increased likelihood of adverse outcomes, including falls, fractures, physical disability, and mortality. By contrast, cachexia is defined as a syndrome characterized by weight loss with the concomitant loss of muscle and/or fat mass. Cancer cachexia leads to functional impairment, reduced physical performance, and decreased survival, and is often accompanied by cancer progression and reduced response to therapy. The literature states that cancer patients with cachexia or sarcopenia have many more complications than patients without these conditions. The interplay between physiologic sarcopenia and cancer cachexia is, in part, responsible for the complexity of studying wasting disorders in the cancer population, particularly in the geriatric population. For these reasons, a comprehensive assessment of the body composition and physical function of these patients is necessary. There are several modalities adapted to measure skeletal muscle mass, such as dual-energy X-ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). The gold standard for the measurement of quantitative and qualitative changes in body composition in patients with cancer is the analysis of tissue density using a CT scan. However, this technique remains poorly implemented in clinical practice because of the use of ionizing radiation. Similarly, DEXA, MRI, and US have been proposed, but their use is limited. In this review, we present and compare the imaging techniques that have been developed so far for the nutritional assessment of cancer patients.
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Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.
Subject(s)
Artificial Intelligence , Liver Neoplasms , Humans , Genomics , Liver Neoplasms/diagnostic imaging , Liver Neoplasms/therapyABSTRACT
Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.
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PURPOSE: The aim of this study was to describe the safety and efficacy profiles of TACE using DC Beads LUMI. MATERIALS AND METHODS: We retrospectively analyzed 90 patients with HCC who underwent TACE with DC Bead LUMI™ between November 2018 and November 2020 at Fondazione IRCCS Cà Granda Policlinico Hospital in Milan, Italy. Patient- and tumour-related factors were registered, including the number of treated lesions, dose of DC Beads LUMI™, dose of Epirubicin, DC Beads LUMI™ target tumour coverage (LC) according to the percentage of target nodule involvement (LC1-0%-25%, LC2-25%-50%, LC3-50%-75%, LC4 75%-100%). Treatment efficacy was obtained through reviewing the follow-up imaging for evidence of response in target lesion(s), according to modified response criteria in solid tumours (mRECIST) criteria with the following outcomes: complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). Safety assessment was based on the quantitative and qualitative recording of the adverse events, classified according to CIRSE classification. RESULTS: Seventy-two patients were enrolled, and 95 procedures were carried out. We observed a target tumour response rate at 1 month with CR in 68%, PR in 10.3% 11.8%, SD in 13%, PD in 7.2%, and an overall tumour(s) (whole liver) response at 1 month with CR in 58.9%, PR in 12.6%, SD in 10.5% and PD in 18%. We found a significant association (p < 0.01) between tumour response CR or CR + PR and the number of the target lesion(s). CIRSE classification grade I and grade II complications were recorded, respectively, in 11 (11.6%) and 6 (6.3%) procedures. No grade III-IV-V complications occurred. CONCLUSION: TACE using DC Beads LUMI is a safe and effective treatment option for patients with HCC.