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BACKGROUND: Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges due to its manifestation variations and requires surgical excision upon histological confirmation of its characteristic cellular features. Recent artificial intelligence (AI) advancements in pathology promise enhanced diagnostic accuracy and treatment approaches for this neoplasm. METHODS: We employed a multiscale transfer learning model, initiating the training process at high resolutions and adapting to broader scales. For evaluation purposes, we applied metrics such as accuracy, precision, recall, the F1 score, and the area under the receiver operating characteristic curve (AUROC) to measure the performance of the model, with the statistical significance of the results assessed via two-sided P tests. Our novel approach also included a retrosynthetic saliency mapping technique to achieve enhanced lesion visualization in whole-slide images (WSIs), supporting pathologists' diagnostic processes. RESULTS: Our model effectively navigated the challenges of global-scale classification, achieving a high validation accuracy of up to 0.973 despite some initial fluctuations. This method displayed excellent accuracy in terms of identifying basaloid and ghost cells, especially at lower scales, with slight variability in its ghost cell accuracy and more noticeable changes in the 'Other' category at higher scales. The consistent performance attained for basaloid cells was clear across all scales, whereas areas for improvement were identified in the 'Other' category. The model also excelled at generating detailed and interpretable saliency maps for lesion visualization purposes, thereby enhancing its value in digital pathology diagnostics. CONCLUSION: Our pilomatricoma study demonstrates the efficacy of a deep learning-based histopathological diagnosis model, as validated by its high performance across various scales, and it is enhanced by an innovative retrosynthetic approach for saliency mapping.

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IMPORTANCE: Ovine pulmonary adenomatosis (OPA) and maedi-visna disease (MVD) are chronic and progressive infectious diseases in sheep caused by Jaagsiekte sheep retrovirus (JSRV) and maedi-visna virus (MVV), respectively. OBJECTIVE: To investigate the pathological changes and conduct viral gene analysis of OPA and MVD co-occurrence in Inner Mongolia, China. METHODS: Using gross pathology, histopathology, immunohistochemistry, ultrastructural pathology, PCR, and sequence analysis, we investigated the concurrent infection of JSRV and MVV in 319 Dorper rams slaughtered in a private slaughterhouse in Inner Mongolia, in 2022. RESULTS: Of the 319 rams included, 3 showed concurrent JSRV and MVV infection. Gross lung pathology showed diffuse enlargement, consolidation, and greyish-white miliary nodules on the lung surface; the trachea was filled with a white foamy fluid; hilar and mediastinal lymph nodes were significantly enlarged. Histopathology results revealed typical OPA and MVD lesions in the lung tissue. Immunohistochemical results were positive for JSRV envelope protein (Env) in the tumor cells and MVV CA in alveolar macrophages. Transmission electron microscopy showed several virions and autophagosomes in the lung tissue, severely damaged mitochondria, and the induced mitophagy. Nucleotide sequences obtained for JSRV env and MVV gag showed the highest homology with the Inner Mongolian strains of JSRV env (JQ837489) and MVV gag (MW248464). CONCLUSIONS AND RELEVANCE: Our study confirmed that OPA and MVD co-occurrence and identified the pathological changes in Inner Mongolia, China, thereby providing references for the identification of concurrent JSRV and MVV infections.

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Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumour caused by the Jaagsiekte Sheep Retrovirus (JSRV). Histopathological diagnosis is the gold standard for OPA diagnosis. However, interpretation of traditional pathology images is complex and operator dependent. The mask regional convolutional neural network (Mask R-CNN) has emerged as a valuable tool in pathological diagnosis. This study utilized 54 typical OPA whole slide images (WSI) to extract 7167 typical lesion images containing OPA to construct a Common Objects in Context (COCO) dataset for OPA pathological images. The dataset was categorized into training and test sets (8:2 ratio) for model training and validation. Mean average specificity (mASp) and average sensitivity (ASe) were used to evaluate model performance. Six WSI-level pathological images (three OPA and three non-OPA images), not included in the dataset, were used for anti-peeking model validation. A random selection of 500 images, not included in the dataset establishment, was used to compare the performance of the model with assessment by pathologists. Accuracy, sensitivity, specificity, and concordance rate were evaluated. The model achieved a mASp of 0.573 and an ASe of 0.745, demonstrating effective lesion detection and alignment with expert annotation. In Anti-Peeking verification, the model showed good performance in locating OPA lesions and distinguished OPA from non-OPA pathological images. In the random 500-image diagnosis, the model achieved 92.8% accuracy, 100% sensitivity, and 88% specificity. The agreement rates between junior and senior pathologists were 100% and 96.5%, respectively. In conclusion, the Mask R-CNN-based OPA diagnostic model developed for OPA facilitates rapid and accurate diagnosis in practical applications.

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Primary diffuse central nervous system large B-cell lymphoma (CNS-pDLBCL) and high-grade glioma (HGG) often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists' diagnostic efforts, yet accurately distinguishing between these conditions is crucial for guiding treatment decisions. This study leverages a deep learning model to classify brain tumor pathology images, addressing the common issue of limited medical imaging data. Instead of training a convolutional neural network (CNN) from scratch, we employ a pre-trained network for extracting deep features, which are then used by a support vector machine (SVM) for classification. Our evaluation shows that the Resnet50 (TL + SVM) model achieves a 97.4% accuracy, based on tenfold cross-validation on the test set. These results highlight the synergy between deep learning and traditional diagnostics, potentially setting a new standard for accuracy and efficiency in the pathological diagnosis of brain tumors.

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Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

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Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.

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Objective: To investigate the clinical characteristics, diagnosis, treatment, and prognosis of primary thyroid lymphoma (PTL) . Methods: A retrospective analysis was conducted on the clinical and pathological data of 34 newly diagnosed PTL patients admitted to Beijing Tongren Hospital from September 2010 to February 2023. The Kaplan-Meier survival curve and Log-rank test were used for survival analysis, and the Cox regression model was applied for univariate analysis of prognostic factors. Results: All 34 PTL patients presented with cervical mass as the initial clinical manifestation. There were 9 males and 25 females. The pathological diagnosis was diffuse large B-cell lymphoma (DLBCL) in 29 patients and mucosa-associated lymphoid tissue (MALT) lymphoma in 5 patients. Among the DLBCL patients, 6 had B symptoms, 17 had an Eastern Cooperative Oncology Group (ECOG) score of ≥2, the Ann Arbor staging was stage Ⅰ-Ⅱ in 21 cases and stage Ⅲ-Ⅳ in 8 cases, the tumor diameter was ≥10 cm in 4 cases, and 14 had concurrent Hashimoto thyroiditis; 27 cases received chemotherapy, with 21 cases achieving complete remission (CR), 2 cases partial remission (PR), and 6 cases of disease progression; the 5-year progression-free survival and overall survival rates were 78.9% and 77.4%, respectively; univariate survival analysis showed that B symptoms, tumor diameter ≥10 cm, and Ann Arbor stage Ⅲ-Ⅳ were significant factors affecting patient prognosis (P<0.05). MALT lymphoma patients were all in stages Ⅰ-Ⅱ, had an ECOG score of 0-1, and were without B symptoms. All patients underwent surgical resection, with 4 cases achieving CR and 1 case PR. Conclusion: PTL is more common in females with concurrent Hashimoto thyroiditis, with the majority of pathological types being B-cell lymphoma. The main treatment is chemotherapy, supplemented by radiotherapy and surgery, and the prognosis is relatively favorable.

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Background: The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. Method: In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Results: Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Conclusion: Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.

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This study investigated the infrequent occurrence of tonsillar follicular dendritic cell sarcoma (FDCS) co-existing with schizophrenia, presenting a comprehensive examination of clinical, pathological, and literature aspects. A systematic literature review was conducted, focusing on articles related to "schizophrenia" and "sarcoma," with in-depth analysis of included case reports. Clinical data, pathological findings, and patient follow-up information were collected and synthesized. The study detailed a rare case of FDCS in the tonsil concurrent with schizophrenia, providing insights into diagnosis, treatment, and follow-up. A literature review of combined FDCS in the tonsil and schizophrenia cases highlighted their clinical and pathological characteristics. Eight case reports encompassing 11 patients diagnosed with sarcoma and schizophrenia were included. Surgical resection was the preferred primary treatment, while chemotherapy was suggested for recurrences. Instances of co-occurring FDCS and schizophrenia were exceptionally limited, with tonsillar FDCS being particularly uncommon. The coexistence of tonsillar FDCS and schizophrenia was an exceptionally rare condition, posing diagnostic and therapeutic challenges. This study contributed valuable insights into clinical and pathological practice through a systematic review, underscoring the significance of early diagnosis and comprehensive management.

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OBJECTIVES: Differentiating gastric atypical hyperplasia (AH) from dysplasia, including low-grade dysplasia (LGD) and high-grade dysplasia (HGD), poses significant challenges in small biopsies and specimens with technical artifacts. This study aims to establish objective diagnostic criteria for these conditions through combined morphologic and immunohistochemical (IHC) analyses. METHODS: Between January 2018 and September 2020, a total of 123 gastric mucosa biopsy specimens were collected at Anyang Tumor Hospital. According to the WHO Classification of Digestive System Tumors (5th edition), specimens were categorized into three groups: AH (n=48), LGD (n=30), and HGD (n=45). Morphologic characteristics were assessed, and IHC staining for MUC5AC, MUC6, MUC2, CD10, P53, and Ki67 was performed, followed by statistical analysis. RESULTS: Histologically, AH was predominantly marked by a pronounced inflammatory background (60.42%), intestinal metaplasia (64.58%), indistinct boundaries (83.33%), and a distinct maturation gradient (97.72%). AH nuclei were typically circular (97.92%), with a high nucleus-to-cytoplasm ratio (64.58%), prominent nucleoli (47.92%), and preserved polarity (89.58%). In contrast, LGD and HGD typically exhibited well-defined boundaries with an absent maturation gradient. LGD nuclei were rod-shaped (96.67%), with a low nucleus-to-cytoplasm ratio (96.67%) and preserved polarity (100%), whereas HGD demonstrated a loss of cellular polarity (77.78%). IHC findings revealed a consistent maturation gradient in AH, with polarized MUC5AC and MUC6 expression, significantly reduced in LGD (86.67%), and absent in HGD. P53 expression in HGD showed a predominant 'mutation-type pattern' (66.67%), contrasting with 'wild-type pattern' expression in AH and LGD (100%, 93.33%). Ki67 expression patterns varied from a 'pit neck pattern' in AH (95.83%) to a 'polarity pattern' in LGD (76.67%) and a 'diffuse pattern' in HGD (57.78%). The expression patterns of MUC5AC, MUC6, CD10, P53, and Ki67 varied significantly across the three groups (P<0.001). CONCLUSIONS: The integration of histomorphological features and expression profiles of MUC5AC, MUC6, P53, and Ki67 is instrumental in diagnosing gastric atypical hyperplasia and dysplasia.

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BACKGROUND: Carcinosarcoma of the gallbladder is a rare malignant tumor with a very poor prognosis. To date, only approximately 100 patients have been reported in the English literature. The prognosis of this tumor type is poor, the preoperative diagnosis is difficult, and there is a possibility of a misdiagnosis. We present an unsuccessful case of carcinosarcoma of the gallbladder with a preoperative misdiagnosis and rapid early postoperative recurrence. Therefore, we have a deeper understanding of the poor prognosis of gallbladder carcinosarcoma (GBC) patients. CASE SUMMARY: The patient is a 65-year-old male. He was admitted to the hospital because of right upper abdomen distending pain and discomfort for half a month. Abdominal magnetic resonance imaging revealed a polycystic mass in the right lobe of the liver and the fossa of the gallbladder. After admission, the patient was diagnosed with a liver abscess, which was treated by abscess puncture drainage. Obviously, this treatment was unsuccessful. Hepatectomy and cholecystectomy were performed one month after the puncture. Postoperative pathologic examination revealed carcinosarcoma of the gallbladder, and the resected specimen contained two tumor components. One month after surgery, the patient's tumor recurred in situ and started to compress the duodenum, resulting in duodenal obstruction and bleeding. The treatment was not effective. The patient died of gastrointestinal hemorrhage and hypovolemic shock. CONCLUSION: Carcinosarcoma of the gallbladder is a rare malignant tumor that is easily misdiagnosed preoperatively and has a poor prognosis.

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INTRODUCTION: There is no consensus regarding the most appropriate management of suspected malignant pulmonary ground-glass nodules (GGNs). OBJECTIVE: We aimed to explore the feasibility and safety of synchronous computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) and microwave ablation (MWA) for patients highly suspicious of having malignant GGNs. METHODS: We retrospectively reviewed medical records between July 2020 and April 2023 from our medical center. Eligible patients synchronously underwent PTNB and MWA (either MWA immediately after PTNB [PTNB-first group] or PTNB immediately after MWA [MWA-first group]) at the the physician's discretion. We analyzed the rate of definitive diagnosis and technical success, the length of hospital stay, the postoperative efficacy, and periprocedural complications. RESULTS: Of 65 patients who were enrolled, the rate of definitive diagnosis was 86.2%, which did not differ when stratified by the tumor size, the consolidation-to-tumor ratio, or the sequence of the two procedures (all p > 0.05). The diagnostic rate of malignancy was 83.1%. After the median follow-up duration of 18.5 months, the local control rate was 98.2% and the rate of completed ablation was 48.2%. The rate of perioperative minor and major complications was 44.6% and 6.2%, respectively. The most common adverse events included pain, cough, and mild hemorrhage. Mild hemorrhage took place significantly less frequently in the MWA-first group than in the PTNB-first group (16.7% vs. 45.5%, p < 0.05). CONCLUSION: Synchronous PTNB and MWA are feasible and well tolerated for patients highly suspicious of having malignant GGNs, providing an alternative option for patients who are ineligible for surgical resection.

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Invasive lobular carcinoma (ILC) is the second most frequent type of breast cancer (BC) and its peculiar morphology is mainly driven by inactivation of CDH1, the gene coding for E-cadherin cell adhesion protein. ILC-specific therapeutic and disease-monitoring approaches are gaining momentum in the clinic, increasing the importance of accurate ILC diagnosis. Several essential and desirable morphologic diagnostic criteria are currently defined by the World Health Organization, the routine use of immunohistochemistry (IHC) for E-cadherin is not recommended. Disagreement in the diagnosis of ILC has been repeatedly reported, but interpathologist agreement increases with the use of E-cadherin IHC. In this study, we aimed to harmonize the pathological diagnosis of ILC by comparing 5 commonly used E-cadherin antibody clones (NCH-38, EP700Y, Clone 36, NCL-L-E-cad [Clone 36B5], and ECH-6). We determined their biochemical specificity for the E-cadherin protein and IHC staining performance according to type and location of mutation on the CDH1 gene. Western blot analysis on mouse cell lines with conditional E-cadherin expression revealed a reduced specificity of EP700Y and NCL-L-E-cad for E-cadherin, with cross-reactivity of Clone 36 to P-cadherin. The use of IHC improved interpathologist agreement for ILC, lobular carcinoma in situ, and atypical lobular hyperplasia. The E-cadherin IHC staining pattern was associated with variant allele frequency and likelihood of nonsense-mediated RNA decay but not with the type or position of CDH1 mutations. Based on these results, we recommend the indication for E-cadherin staining, choice of antibodies, and their interpretation to standardize ILC diagnosis in current pathology practice.

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In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th), was published. Large-scale genomic analyses such as The Cancer Genome Atlas (TCGA) have revealed the importance of understanding the molecular genetics of thyroid tumors. Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on the cell of origin of tumors and their clinical risk. This paper outlines the following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDNs) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The framework for benign lesions has been revised. The WHO 5th introduces a new category: "developmental abnormalities". Benign FDNs comprise "thyroid follicular nodular disease", follicular thyroid adenoma (FTA), FTA with papillary architecture, and oncocytic adenoma (OA). "Hürthle cell adenoma/carcinoma" is renamed oncocytic adenoma/carcinoma of the thyroid (OA/OCA), which can be distinguished from FTA/FTC by its unique genetic background. 3. Low-risk tumors include NIFTP, TT-UMP, and HTT, and they have an extremely low malignant potential or an uncertain malignant potential. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to their shared genetic and prognostic features. 7. Other miscellaneous tumors are categorized as salivary-gland-type carcinomas of the thyroid, thyroid tumors of uncertain histogenesis, thymic tumors within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on both genetic abnormalities and histomorphology. This approach aids in achieving accurate pathological diagnosis and facilitates the early selection of appropriate treatment options, including molecular targeted therapies.

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The usefulness of comprehensive genomic profiling (CGP) in the Japanese healthcare insurance system remains underexplored. Therefore, this large-scale study aimed to determine the usefulness of CGP in diagnosing digestive cancers. Patients with various cancer types recruited between March 2020 and October 2022 underwent the FoundationOne® CDx assay at the Keio PleSSision Group (19 hospitals in Japan). A scoring system was developed to identify potentially actionable genomic alterations of biological significance and actionable genomic alterations. The detection rates for potentially actionable genomic alterations, actionable genomic alterations, and alterations equivalent to companion diagnosis (CDx), as well as the signaling pathways associated with these alterations in each digestive cancer, were analyzed. Among the 1587 patients, 547 had digestive cancer. The detection rates of potentially actionable genomic alterations, actionable genomic alterations, and alterations equivalent to CDx were 99.5%, 62.5%, and 11.5%, respectively. APC, KRAS, and CDKN2A alterations were frequently observed in colorectal, pancreatic, and biliary cancers, respectively. Most digestive cancers, except esophageal cancer, were adenocarcinomas. Thus, the classification flowchart for digestive adenocarcinomas proposed in this study may facilitate precise diagnosis. CGP has clinical and diagnostic utility in digestive cancers.

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BACKGROUND: Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS: We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS: We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS: We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

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The Japanese Breast Cancer Society Clinical Practice Guidelines are published as timely guidance on clinical issues in breast cancer treatment in Japan. In the recent edition of these guidelines, we addressed a new clinical question 34 (CQ 34, systemic treatment part) "Is trastuzumab deruxtecan recommended for patients with unresectable or metastatic HER2-low breast cancer?" and a new future research question 7 (FRQ 7, pathological diagnosis part) "How is HER2-low breast cancer diagnosed for the indication of trastuzumab deruxtecan?". These questions address use of trastuzumab deruxtecan in patients with unresectable or metastatic HER2-low breast cancer who have previously received chemotherapy for metastatic disease. The strengths of evidence and recommendation were determined through a quantitative and qualitative systematic review using multiple outcomes, including efficacy and safety. We conclude that trastuzumab deruxtecan is recommended for this patient population (strength of recommendation: 1; strength of evidence: moderate; CQ34) and that HER2-low expression for the indication of trastuzumab deruxtecan should be diagnosed using companion diagnostics based on appropriate criteria (FRQ7).

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AIMS: We aimed to assess the oncological impact of micrometric extent of invasion in patients with pT1 bladder cancer (BCa) who underwent en-bloc resection for bladder tumour (ERBT). METHODS AND RESULTS: We retrospectively analysed the records and specimens of 106 pT1 high-grade BCa patients who underwent ERBT. The extent of invasion, such as depth from basal membrane, number of invasive foci, maximum width of invasive focus, muscularis mucosae invasion and infiltration pattern (pattern A: solid sheet-like, nodular or nested growth, pattern B: trabecular, small cluster or single-cell pattern) were evaluated by a single genitourinary pathologist. The end-points were recurrence-free (RFS) and progression-free survival (PFS). Within a median follow-up of 23 months, overall, 36 patients experienced recurrence and 13 patients experienced disease progression. The 2-year PFS differed significantly depending on depth from basal membrane (< 1.3 mm: 94.8% versus ≧ 1.3 mm: 65.2%, P = 0.005), maximum width of invasive focus (< 4 mm: 91.7% versus ≧ 4 mm: 62.3%, P < 0.001), muscularis mucosae (MM) invasion (above MM = 96.1% versus into or beyond MM = 64.8%, P = 0.002) and infiltration pattern (pattern A: 100% versus pattern B: 83.3%, P = 0.037). In a multivariable analysis, MM invasion [hazard ratio (HR) = 4.54, 95% confidence interval (CI) = 1.25-16.5] and maximum width of invasive focus ≧ 4 mm (HR = 4.79, 95% CI = 1.25-16.5) were independent prognostic factors of progression. CONCLUSIONS: En-bloc resection facilitates the evaluation of pathologic variables that might be useful in predicting disease recurrence and progression. In particular, not only the MM invasion but also the maximum width of invasion focus, reflecting the invasive volume, appear to be reliable prognosticators for disease progression.

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Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a unique type of liver tumor that contains both hepatocellular carcinoma and cholangiocarcinoma components within a single tumor. The fifth edition of the World Health Organization classification provides a definition and diagnostic criteria for cHCC-CCA. However, the heterogeneous histomorphology and presentation resulting from variation of the proportion of each component poses challenges for clinical diagnosis and treatment. A diagnosis of cHCC-CCA may be suggested by the synchronous elevation of serum tumor markers for hepatocellular carcinoma and cholangiocarcinoma, a mixed enhancement pattern on imaging, and a discrepancy between the elevation of tumor marker and the imaging enhancement pattern. Histopathological examination using hematoxylin and eosin staining is considered the gold standard for diagnosing cHCC-CCA, and comprehensive examination of resection or biopsy specimens is crucial for an accurate diagnosis. Currently, there is no standard treatment for cHCC-CCA, and surgery is the mainstay. Anatomic hepatectomy with lymphadenectomy is among the recommended surgical procedures. The role of liver transplantation in the management of cHCC-CCA is still uncertain. Transarterial chemoembolization may be effective for unresectable cHCC-CCA, particularly for hypervascular tumors. However, the available evidence does not support systemic therapy for advanced cHCC-CCA. The prognosis of cHCC-CCA is generally poor, and there is no established staging system. Further research is needed to better understand the histogenesis and clinical management of cHCC-CCA. This review provides an overview of the current literature on cHCC-CCA with a focus on its clinical characteristics, pathological diagnosis, and management.