Extramammary Paget's disease (EMPD) is a rare cutaneous adenocarcinoma that remains clinically challenging in advanced or metastatic stages, yet the molecular mechanisms underlying tumour invasion and metastasis remain poorly understood. SOX9 has emerged as a critical factor in tumour invasion and metastasis across several cancers, but its role in EMPD has not been systematically characterized. In this study, we established a cohort of 93 patients with genital EMPD and performed integrated transcriptomic and proteomic profiling to assess the expression and clinical significance of SOX9. We found that elevated SOX9 transcript levels were significantly associated with tumour invasion, whereas increased SOX9 protein abundance correlated with metastatic progression. To explore the potential functional relevance, we established a SOX9-overexpressing cellular model as a preliminary approach and performed combined transcriptomic and extracellular matrix (ECM) proteomic analyses. Functionally, SOX9 overexpression enhanced cellular migratory capacity in vitro. Both transcriptomic and proteomic profiles converged on the regulation of extracellular matrix organisation, with SOX9 overexpression associated with increased expression of ECM-related genes including MMP1, MMP10 and MMP12, as well as increased abundance of ECM proteins such as FMOD and COL1A2. Collectively, these findings suggest that SOX9 may serve as a promising indicator of aggressive clinicopathological features in EMPD and provide preliminary evidence linking its overexpression to ECM-related molecular changes and enhanced cellular migratory capacity in vitro. Further validation in independent cohorts and functional models is warranted.

A man in his late 60s presented with macroscopic haematuria and a lump in the right groin that partially resolved post micturition. Clinical examination confirmed an incarcerated right inguinal hernia while imaging showed a right inguinal hernia containing part of the dome of the bladder. At cystoscopy, three papillary tumours were seen in the part of the bladder within the hernia, with the remainder of the bladder found to be normal. The patient was managed with an open right inguinal hernia repair and partial cystectomy with pelvic node dissection. Inguinal hernias containing part of the bladder are uncommon and neoplastic change in such herniated bladder is rare. We present this case to show that the management of such patients requires individualised treatment.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with most patients requiring systemic therapy at diagnosis or during disease progression. Immune checkpoint inhibitor (ICI)-based combinations have transformed the therapeutic landscape and are now the standard first-line treatment, demonstrating improved survival and higher response rates compared with earlier tyrosine kinase inhibitor-based approaches. Attempts to further improve outcomes through triplet immunotherapy strategies have so far failed to show clear clinical benefit and are often associated with increased toxicity. Emerging therapeutic approaches - including bispecific antibodies, cellular therapies, cancer vaccines, oncolytic viruses, and novel molecularly targeted agents - are currently under clinical investigation. However, the biological heterogeneity of HCC and the lack of robust predictive biomarkers remain major challenges. This review summarises current systemic therapies for advanced stages of HCC and discusses emerging strategies and biomarker approaches that may enable more precise and effective treatment selection in the future.

The treatment of advanced primary liver cancer has seen major improvement in recent years. In hepatocellular carcinoma (HCC), immunotherapy has improved both survival and quality of life. In biliary tract cancers (BTC), immunotherapy has shown modest benefits, while molecular targeted therapies have had a major impact in selected populations defined by molecular alterations. These advances are now being tested at earlier disease stages. In this review, we first discuss the challenges in designing trials that combine local and systemic treatment at earlier stages, and the different endpoints that might be used. We then present the available data on the combination of intra-arterial and systemic treatments in HCC. We continue with a discussion of the available data on adjuvant and neoadjuvant systemic treatment in HCC. Finally, we review recent developments in the adjuvant and neoadjuvant settings for BTC. While promising data exist across these settings, uncertainties remain regarding whether these strategies will become standard of care in the coming years.

The standard of care for patients with unresectable or advanced cholangiocarcinoma (CCA) is systemic chemotherapy combined with immunotherapy, which achieves a median overall survival (OS) of 13 months and a 3-year OS of 15%. This provides the benchmark against which additional locoregional therapies are evaluated. This narrative literature review examined locoregional therapies, their clinical outcomes, and recommendations for their role in the management of patients with CCA. The review focused on intrahepatic (iCCA) and perihilar CCA (pCCA) with locoregional disease in patients who were ineligible for surgical resection. A literature search was conducted in PubMed (MEDLINE) to identify clinical studies evaluating locoregional therapies in CCA. Ongoing and completed trials were identified through ClinicalTrials.gov. For iCCA, clinical trials have evaluated percutaneous image-guided local ablative therapies, including radiofrequency ablation or microwave ablation (3-year OS 42%), cryoablation, interstitial electroporation, and brachytherapy. Other trials have analysed external beam radiotherapy (EBRT), often using high-precision techniques (3-year OS 38%), as well as intra-arterial therapies, including transarterial chemoembolisation (3-year OS 17%), selective internal radioembolisation (3-year OS 40%), and floxuridine delivery through a hepatic arterial infusion pump (3-year OS 26-50%). For pCCA, EBRT has been evaluated in three treatment settings: definitive therapy (1-year OS 100%), adjuvant postoperative therapy (3-year OS 58%), and neoadjuvant therapy before liver transplantation (3-year OS 62%). Recent and ongoing clinical trials demonstrate a clear trend toward combining locoregional and standard-of-care systemic therapies. Few randomised controlled trials have been conducted. Until further evidence from randomised controlled trials becomes available, treatment strategies for patients with unresectable or advanced CCA should be individualised, incorporating flexible, staged combinations of locoregional and systemic therapies within a personalised therapeutic framework. These decisions should be made within a multidisciplinary tumour board.

Liver transplantation (LT) remains the most effective curative-intent surgical approach for selected patients with primary hepatic malignancies, owing to its ability to completely replace the diseased liver and address both the tumour and the underlying liver disease. Rather than being determined solely by technical feasibility, transplant candidacy is defined by a dynamic "window of eligibility" based on predicted overall survival and transplant benefit, with repeated assessment of tumour burden, biology, and response to therapy. LT should therefore be viewed as a complex, multi-step process encompassing patient selection, neoadjuvant treatment, graft procurement, surgery, and long-term postoperative management. Neoadjuvant strategies, including bridging, downstaging, and conversion therapies, have become central to patient selection and have expanded transplant eligibility for selected patients with advanced disease through combinations of systemic and locoregional treatments. Following transplantation, minimisation of immunosuppression is essential to balance graft preservation against tumour immune surveillance, although the persistence of antitumour immunity after pre-transplant immunotherapy remains uncertain. Emerging biomarkers, including circulating tumour DNA, circulating tumour cells, and microRNAs, have the potential to improve candidate selection, detect minimal residual disease, and enable earlier, biology-driven identification of recurrence, thereby supporting a more personalised approach to LT in primary liver cancers.

Liver cancer remains a major global health burden, with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) accounting for the vast majority of primary liver malignancies. As most patients are diagnosed at advanced stages, imaging plays a central role across the entire care continuum - from surveillance and early detection to diagnosis, staging, prognostic stratification, and treatment response assessment. Recent advances in imaging technology, diagnostic frameworks, and computational analysis have substantially expanded the clinical value of liver imaging. This review summarises key developments in liver cancer imaging, beginning with technical innovations in ultrasound, multiphasic and spectral CT, MRI with hepatobiliary contrast agents, abbreviated MRI protocols, and evolving PET tracers such as FAPI. We then examine the convergence and remaining differences among major diagnostic guideline systems for HCC, including LI-RADS, EASL, AASLD, KLCA-NCC, and APASL, with emphasis on vascular hallmarks, hepatobiliary phase interpretation, and Kupffer-phase contrast-enhanced ultrasound criteria. We also discuss the concept of the HCC-iCCA biological continuum, and how imaging features can reflect this phenotypic spectrum. A major focus is the emerging concept of imaging-based prognostication of HCC, which captures biologically meaningful phenotypes such as proliferative class, macrotrabecular-massive subtype, microvascular invasion likelihood, immune microenvironment signatures, and VETC phenotype. We discuss how these imaging features reflect underlying molecular programmes and may inform treatment allocation beyond tumour size and number. In iCCA, advances in quantitative imaging and contrast-enhanced ultrasound perfusion enable differentiation of small-duct vs. large-duct subtypes and prediction of stromal-rich aggressive variants. Treatment response assessment criteria are reviewed, addressing challenges in evaluating locoregional therapies and immunotherapy. Finally, we examine the emerging roles of radiomics and artificial intelligence, focusing on current validation challenges. Together, these advances demonstrate the evolution of liver cancer imaging from a detection tool to a comprehensive platform for biological characterisation and personalised treatment guidance.

Mutations in the tumor protein 53 gene (TP53 mutation) in thyroid nodules are quoted to confer a high (80%) probability of malignancy when detected on the ThyroSeq v3 genomic classifier if associated with other molecular alterations. However, TP53 mutation also occurs in benign and low-risk thyroid neoplasms. Thus, the risk of malignancy in nodules harboring TP53 mutation is not well characterized.

Of 4,575 molecularly profiled preoperative fine-needle aspiration samples, 36 (0.8%) were identified harboring TP53 mutation. The study included 32 cases in which the pathology diagnosis was obtained from surgical specimens.

The reviewed diagnosis was benign/low-risk neoplasms in 11 (34%), carcinoma-American Thyroid Association low risk of recurrence in 7 (22%), carcinoma-American Thyroid Association low-intermediate risk in 6 (19%), and carcinoma-American Thyroid Association high risk in 8 (25%). In the entire cohort and the indeterminate fine-needle aspiration category (Bethesda III-IV), the risk of malignancy was 66% and 56%, respectively. All 11 cases with a reviewed diagnosis of benign or low-risk neoplasms had their tumor capsule submitted entirely for histologic examination, and 64% had total thyroidectomy. In 30 cases comprehensively molecularly profiled, the molecular alterations were substratified into 4 groups: TP53 mutation alone (n = 5, 17%), TP53 mutation with copy number alteration (n = 9, 30%), TP53 mutation with other mutations but no copy number alteration (n = 9, 30%), and TP53 mutation with other mutations and copy number alteration (n = 7, 23%). The risk of malignancy for each group was 40%, 44%, 67%, and 100%, respectively. The frequency of American Thyroid Association-high-risk malignancy, which would often lead to a recommendation for total thyroidectomy, was 20%, 11%, 22%, and 43%, respectively. The risk of malignancy was higher in cases with additional mutations and copy number alteration (7/7, 100%) than in those with TP53 alone or with concomitant copy number alteration only (6/14, 43%) (P = .018).

Thirty four percent of nodules with TP53 mutation with or without concomitant molecular alterations were benign/low-risk thyroid neoplasms and treated by total thyroidectomy in the majority of cases. Risk of malignancy increased significantly to 100% when TP53 mutation co-occurred with other mutations and copy number alterations. Since American Thyroid Association high-risk carcinomas were found in only 25% of TP53-mutated nodules, thyroid lobectomy may be considered as the initial treatment, in the appropriate clinical context.

The rising global incidence of renal tumors necessitates precise diagnostic interventions. Accurate segmentation of computed tomography (CT) scans is essential for nephron-sparing surgery and radiotherapy. However, conventional manual delineation is labor-intensive and prone to significant interobserver variability due to tumor morphological heterogeneity. There is an urgent clinical demand for robust, automated segmentation solutions.

This study aims to develop and validate GAM-DeepLabV3+, an automated framework designed to address boundary ambiguity and high false-positive rates in complex renal imaging scenarios.

We propose an optimized encoder-decoder architecture specifically tailored for renal mass detection. The framework incorporates three key innovations: (1) a lightweight MobileNetV2 backbone to minimize computational overhead for clinical deployment; (2) an Atrous Spatial Pyramid Pooling (ASPP) module to capture multiscale contextual information; and (3) a Global Attention Mechanism (GAM) in the decoder to enhance channel-spatial interactions, thereby refining boundary delineation by suppressing background noise. The model was rigorously evaluated on a private clinical dataset (n=218) and the KiTS19 benchmark (n=210).

GAM-DeepLabV3+ consistently outperformed state-of-the-art baselines. On the private dataset, the model achieved a mean Dice similarity coefficient (DSC) of 0.939 (SD 0.008), significantly surpassing feature pyramid network (FPN; mean 0.893, SD 0.008; P<.001) and no-new-Net (nnU-Net; 2D, custom; mean 0.908, SD 0.013; P<.001). It also achieved a mean 95% Hausdorff distance (HD95) of 1.485 (SD 0.522) pixels. On the KiTS19 dataset, it maintained a mean robust DSC of 0.928 (SD 0.006). To facilitate clinical translation, a demonstration-only online platform was developed.

The GAM-DeepLabV3+ framework provides an accurate, efficient, and fully automated solution for renal tumor segmentation. By overcoming boundary ambiguity and optimizing feature fusion, this approach shows potential as a decision-support aid, pending future validation with 3D reconstruction.

Cancer diagnosis depends on data from radiology, digital pathology, molecular profiling, laboratory testing, and longitudinal clinical records. AI performs well in selected tasks, but most systems remain narrow and disconnected from the iterative reasoning required in oncology. This Viewpoint defines an AI agent as a feedback-driven system that maintains task state, selects among governed tools, observes results, and revises its plan under explicit safety constraints. This definition separates agents from multimodal foundation models, retrieval-augmented generation, and fixed workflow automation. We organize the discussion across multimodal data collection, preprocessing, fusion and representation learning, and diagnostic decision support. We distinguished agent-level evidence, component- or infrastructure-level evidence, and prospective propositions throughout. Clinical translation will require resilient failure handling, guideline version control, prospective evaluation, computational and workflow feasibility, and clinician authority over final decisions. The near-term opportunity is therefore transparent and traceable clinical decision support rather than autonomous cancer diagnosis.