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Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact. This review is designed for the new generation of radiologists, who may not have specialized training in machine learning or radiomics, but will inevitably play a role in this evolving field. The paper has two primary objectives: first, to provide a clear, systematic guide to radiomics study pipeline, including study design, image preprocessing, feature selection, model training and validation, and performance evaluation. Furthermore, given the critical importance of evaluating the robustness of radiomics studies, this review offers a step-by-step guide to the application of the METhodological RadiomICs Score (METRICS, 2024)—a newly proposed tool for assessing the quality of radiomics studies. This roadmap aims to support researchers and reviewers alike, regardless of their machine learning expertise, in utilizing this tool for effective study evaluation. [ABSTRACT FROM AUTHOR]

Simple Summary: The indications for neoadjuvant chemotherapy (NAT) have expanded in recent years both for locally advanced and early-stage breast cancer, with different pathological complete response (pCR) rates among different molecular subtypes. This retrospective two-center study aims to evaluate the diagnostic performance of Contrast-Enhanced Mammography (CEM) in assessing the response to NAT in breast cancer patients. The CEM sensitivity and specificity were 66.2% and 75.2%, with the highest specificity (80.9%) in HR+/HER2− and the highest sensitivity (70%) in triple-negative breast cancer. CEM is a valid tool to assess the pCR with different performances among the molecular subtypes and may be reliable in the decisional process of de-escalating surgical management. Neoadjuvant chemotherapy (NAT) plays a crucial role in breast cancer (BC) treatment, both in advanced BC and in early-stage BC, with different rates of pathological complete response (pCR) among the different BC molecular subtypes. Imaging monitoring is mandatory to evaluate the NAT efficacy. This study evaluates the diagnostic performance of Contrast-Enhanced Mammography (CEM) in BC patients undergoing NAT. This retrospective two-center study included 174 patients. The breast lesions were classified based on the molecular subtypes in hormone receptor (HR+)/HER2−, HER2+, and triple-negative breast cancer (TNBC). The histopathological analysis performed following surgery was used as a reference standard for the pCR. Sensitivity, specificity, PPV, and NPV were measured overall and for the different subtypes. We enrolled 174 patients, 79/174 (46%) HR+/HER2−, 59/174 (33.9%) HER2+, and 35/174 (20.1%) TNBC; the pCR was found in 64/174 (36.8%), of which 57.1% were TNBCs. In the total population, the CEM sensitivity and specificity were 66.2% and 75.2%, with a PPV of 61.4% and an NPV of 78.8%. The highest specificity (80.9%) and NPV (91.7%) were found in HR+/HER2−, while the highest sensitivity (70%) and PPV appeared (73.7%) in TNBC. The results indicate that CEM is a valid tool to assess the pCR, with different performances among the subtypes of BC. [ABSTRACT FROM AUTHOR]

Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients' emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies. [ABSTRACT FROM AUTHOR]

Featured Application: This systematic review can serve as an educational resource for clinicians, radiologists, and other healthcare professionals. It summarizes the current state of evidence, highlights key issues, and provides an overview of the strengths and limitations of ultrasound elastography for renal masses. This promotes awareness and understanding of the technique in the medical community. The incidental finding of small renal masses in CT and MRI examinations can present a diagnostic challenge. Renal cell carcinoma (RCC) and angiomyolipoma (AML) are the most common incidental malignant and benign renal lesions but may present with similar US features. US elastography is a non-invasive technique that can assess tissue elasticity, has shown promising results in many clinical settings, and could be able to differentiate between benign and malignant renal lesions based on tissue stiffness. The purpose of this article is to systematically review the applications of US elastography in the characterization of solid renal masses and to derive and compare the summary estimates of different stiffness values across different lesion subtypes. In December 2022, a systematic search was carried out on the MEDLINE (PubMed) and EMBASE databases to retrieve studies on the application of US elastography in the characterization of solid renal masses. After article selection by three researchers, 14 studies entered qualitative synthesis. A total of 1190 patients were included, and the elastography data of 959 lesions were examined: 317/959 (33%) benign and 642/959 (67%) malignant. Among the malignancies, 590 (91%) were RCC, whereas, among the 317 benign lesions, 244 (77%) were AML. All lesions were classified using a histopathological (biopsy or operative specimen) or imaging (US follow-up/CT/MRI) reference standard. After data extraction and methodological quality evaluation, quantitative synthesis was performed on 12 studies, 4 using strain elastography (SE) and 8 using shear wave elastography (SWE), with single- and double-arm random-effects meta-analyses. Lesion stiffness measured with SE was available in four studies, with an RCC strain ratio higher than the AML strain ratio both in an indirect comparison (Cochran's Q test p = 0.014) and in a direct comparison (p = 0.021). Conversely, the SWE measurements of RCC and AML stiffness did not significantly differ either at an indirect comparison (p = 0.055) or direct comparison (p = 0.114). [ABSTRACT FROM AUTHOR]

Lymphedema is a chronic progressive disorder that significantly compromises patients' quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema. [ABSTRACT FROM AUTHOR]

Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.