Diagnostic Pathology最新文献

Background: The global incidence of thyroid cancer has significantly increased, while traditional pathological diagnosis remains time-consuming and expert-dependent. This study develops an auxiliary diagnostic tool designed to reduce the workload of pathologists and improve diagnostic accuracy.

Methods: Our study utilized 543 WSIs from Liuzhou Cancer Hospital for model development, employing a novel multi-feature fusion architecture that combines RetCCL, iBOT, and DINO embeddings. We systematically evaluated stain normalization and multi-scale analysis across four multiple-instance learning (MIL) frameworks: CLAM-SB (single-branch), CLAM-MB (multi-branch), DTFD (double-tier), and LA-MIL (location-aware). The method was rigorously validated on an independent set of 128 WSIs from Taizhou Cancer Hospital.

Results: The results show that stain normalization, multi-scale fusion, and multi-feature fusion significantly improve classification performance. In 10-fold cross-validation on the internal dataset, the system demonstrated significant improvements over the baseline RetCCL model: AUC (0.9900 vs. 0.9629), accuracy (0.9594 vs. 0.8951), with relative improvements of 2.8% in AUC and 7.2% in accuracy. Precision increased by 11.5% (0.9434 vs. 0.8461) and F1-score by 9.8% (0.9511 vs. 0.8665). On the external validation dataset, the model maintained robust performance with an AUC of 0.9584, accuracy of 0.9070, precision of 0.9247, and F1-score of 0.9348, confirming its reliability and applicability.

Conclusions: We propose a weakly supervised MIL framework integrating multi-scale analysis and cross-model feature fusion for thyroid cancer diagnosis. Our method showed promising and consistent results across internal and external datasets. While further clinical validation and workflow integration are needed, the results suggest the potential of this approach to assist pathologists in diagnostic workflows, particularly in resource-constrained settings.

Background: NTRK-rearranged spindle cell neoplasms constitute a novel, heterogeneous group of mesenchymal neoplasms originally described predominantly in soft tissue locations. They are commonly characterized by co-expression of S100 and CD34 immunostains and presence of NTRK fusions. While exceedingly rare, there are increasing reports of this lesion involving the gastrointestinal tract, presenting predominantly as large masses of the stomach, small bowel and colorectum.

Case presentation: We present a case of a 37-year-old male who on colonoscopy was found to have a one cm polyp of the sigmoid colon which was removed by hot snare polypectomy. Histologic examination revealed haphazardly arranged bland spindle cells with diffuse CD34 and S100 co-expression. A targeted Next-Generation RNA Fusion Assay identified a TPR::NTRK1 fusion, confirming the diagnosis of low-grade NTRK-rearranged spindle cell neoplasm. The mucosal and deep margins were free of tumor. In contrast to the previously reported cases, the patient was managed with polypectomy and active surveillance, and remained disease-free at 14 months follow up.

Conclusion: This case contributes to the limited body of literature on gastrointestinal low-grade NTRK-rearranged spindle cell neoplasms and raises the possibility of endoscopic treatment consideration for carefully selected patients.

Background: Primary pulmonary malignant melanoma (PMML), an exceedingly rare aggressive neoplasm originating from bronchial mucosal melanocytes, is characterized by early metastatic dissemination and high mortality. While over 95% of malignant melanomas are cutaneous in origin, fewer than 80 PMML cases have been documented globally. The molecular pathogenesis of PMML remains poorly defined, with less prior genomic studies utilizing Next-generation sequencing (NGS) reported to date.

Case presentation: A 68-year-old asymptomatic woman was referred to our institution in June 2022 after a routine health screening revealed a solitary pulmonary nodule. Chest CT demonstrated a 1.2 cm × 0.8 cm hypodense nodular opacity nodule in the posterior segment of the left upper lobe. The lesion remained stable during a 2-month observation period. Despite the absence of respiratory symptoms (e.g., cough, hemoptysis) or constitutional signs (e.g., weight loss), the patient elected surgical resection due to persistent malignancy concerns.

Conclusion: Histopathological examination revealed tumor cells exhibiting epithelioid to spindle-shaped morphology, characterized by prominent nucleoli and intracytoplasmic melanin deposition (hematoxylin and eosin staining). Immunohistochemical analysis demonstrated diffuse and strong positivity for S-100, HMB-45, and Melan-A. Based on the histomorphological features and immunohistochemical profile, a diagnosis of malignant melanoma was established. NGS detected a somatic KIT exon 11 mutation (c.1727 T > C, p. Leu576Pro; variant allele frequency: 20.1%) and identified an SRD5A3-KIT gene fusion involving transcript variants NM_024592.4 (SRD5A3) and NM_000222.2 (KIT), with breakpoints in Exon 5 of SRD5A3 and Exon 6 of KIT. The fusion variant showed a somatic mutation frequency of 24.8%. These findings not only expand the molecular landscape of PMML but also suggest therapeutic opportunities through targeted kinase inhibition. This case underscores the critical role of integrated multimodal analysis (radiological-pathological-molecular) in characterizing rare malignancies.

Introduction: Primary hyperparathyroidism (PHPT) is a prevalent endocrine disorder characterized by elevated parathyroid hormone (PTH) levels and hypercalcemia, most commonly caused by solitary adenomas. Double adenomas, particularly those arising in ectopic and supernumerary glands, represent a rare diagnostic and surgical challenge.

Case presentation: We report the case of a 64-year-old woman presenting with symptomatic PHPT. Preoperative imaging demonstrated uptake consistent with two hyperfunctioning parathyroid adenomas, including a rare supernumerary ectopic adenoma in lesion the right parotid region. Definitive diagnosis and surgical planning were guided by 18 F-fluorocholine PET/CT, which proved superior to conventional modalities.

Discussion: This case underscores the critical role of advanced imaging techniques in the localization of parathyroid adenomas, particularly in anatomically atypical sites. The combination of functional and anatomical imaging with 18 F-fluorocholine PET/CT enabled accurate detection of both lesions and informed a multidisciplinary surgical approach.

Conclusion: Integration of 18 F-fluorocholine PET/CT into the diagnostic workflow enhances the precision of parathyroid adenoma localization, especially in rare ectopic presentations. This contributes to tailored surgical strategies and improved patient outcomes. Histopathological examination confirmed two distinct adenomas, including one embedded in the parotid gland, supporting the diagnosis of a supernumerary ectopic parathyroid adenoma.

Background: Paragangliomas are neuroendocrine tumors that often present as solitary tumors. In this case report, we describe a patient with multiple head and neck paraganglioma associated with a mediastinal paraganglioma.

Case presentation: The patient was a 46-year-old male with a history of surgical removal of a mass from the right side of the neck, who presented with dysphonia lasting two months, hoarseness, vague chest pain, and unilateral ptosis. CT angiography of the carotid arteries and thoracic aorta revealed multiple findings, including a well-defined enhancing mass measuring 33 × 39 mm in the aorto-pulmonary prevascular space, a grade I carotid body tumor on the left side of the neck, vagal paragangliomas on the right side of the neck, and a glomus jugulare tumor on the right side. These findings were collectively suggestive of multiple paragangliomas. The patient subsequently underwent surgical resection of the mediastinal tumor, and pathological examination confirmed the diagnosis of paraganglioma.

Conclusion: This report details a rare case of paraganglioma with multiple head, neck, and mediastinal involvement, emphasizing the need for thorough evaluation and genetic assessment in atypical presentations.

Background: Prostate cancer (PCa) is one of the most common malignancies affecting men, with primary treatments involving surgery, radiotherapy, and hormonal therapy. The introduction of precision medicine and next-generation sequencing (NGS) has profoundly influenced the clinical management of PCa, particularly by enabling the assessment of genetic alterations that guide treatment decisions. Liquid biopsy using diverse sample types, including plasma, urine, and semen, offers non-invasive alternatives to tissue biopsies. This study sought to compare the performance of NGS-based mutation detection across various sample types in PCa patients.

Methods: Thirty-seven PCa patients, diagnosed with intermediate to advanced stages (II-IV), were enrolled. All collected samples, including tissues (n = 34), plasma (n = 37), urine (n = 32), and seminal fluids (n = 9), underwent targeted NGS of 437 cancer-related genes. The detection sensitivity, mutational landscape, and maximum variant allele frequencies (MVAFs) were compared across different sample types.

Results: Tissue samples, serving as the gold standard, achieved a 100% mutation detection rate. Plasma and urine samples demonstrated high detection sensitivities, reaching 67.6% and 65.6%, respectively, while semen samples showed a lower detection rate of 33.3%. Mutations in FOXA1, SPOP, and TP53 were commonly detected across most sample types with comparable prevalence. AR mutations were observed with similar frequencies in plasma and semen samples, but were absent in tissue and urine samples. The average MVAFs were at similar levels among tissue, plasma, urine, and semen, although urine sediment samples exhibited the lowest MVAFs. Advanced disease stages correlated with increased circulating tumor DNA (ctDNA) detection in both plasma and urine samples. No significant survival advantage associated with ctDNA negativity was observed, likely due to the small sample size.

Conclusions: This study validates the utility of urine and plasma samples as non-invasive and sensitive liquid biopsy options for PCa, showing comparable ctDNA detection rates. Seminal fluid samples also demonstrate potential, despite current sampling challenges. These findings offer insights into the advantages of different sampling methods for PCa detection and reinforce the clinical utility of liquid biopsies in PCa management.

Objectives: To report a rare case of adult gastroduodenal intussusception caused by a gastric gastrointestinal stromal tumor (GIST) and review its diagnostic and therapeutic approaches.  METHODS: We present a 68-year-old female with gastroduodenal intussusception secondary to a gastric GIST, diagnosed through combined endoscopy and computed tomography (CT). A systematic PubMed review identified 28 published cases, which were analysis for clinical presentation, imaging findings, and management strategies.

Results: The patient initially underwent laparoscopic-endoscopic cooperative surgery, which was unsuccessful and required conversion to open partial gastrectomy. Intraoperative findings confirmed a fundus mass extending into the duodenum, with histopathology confirming a low-risk GIST. Among the reviewed cases, all involved GISTs of gastric origin. Diagnostic evaluation consistently relied on CT and endoscopy, with surgical approaches varying based on tumor characteristics.

Conclusions: Gastric GISTs are a rare but clinically significant cause of adult gastroduodenal intussusception, typically necessitating surgical intervention. Multimodal imaging, particularly CT, plays a crucial role in preoperative diagnosis, while histopathological examination remains essential for definitive diagnosis and risk stratification. Treatment should be individualized based on tumor size, location, and patient factors.

Background: Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs).

Methods: A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status.

Results: The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases.

Conclusion: This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.

Medical healthcare has advanced substantially due to advancements in Artificial Intelligence (AI) techniques for early disease detection alongside support for clinical decisions. However, a gap exists in widespread adoption of results of these algorithms by public due to black box nature of models. The undisclosed nature of these systems creates fundamental obstacles within medical sectors that handle crucial cases because medical practitioners needs to understand the reasoning behind the outcome of a particular disease. A hybrid Machine Learning (ML) framework integrating Explainable AI (XAI) strategies that will improve both predictive performance and interpretability is explored in proposed work. The system leverages Decision Trees, Naive Bayes, Random Forests and XGBoost algorithms to predict the medical condition risks of Diabetes, Anaemia, Thalassemia, Heart Disease, Thrombocytopenia within its framework. SHAP (SHapley Additive exPlanations) together with LIME (Local Interpretable Model-agnostic Explanations) adds functionality to the proposed system by displaying important features contributing to each prediction. The framework upholds an accuracy of 99.2% besides the ability to provide understandable explanations for interpretation of model outputs. The performance combined with interpretability from the framework enables clinical practitioners to make decisions through an understanding of AI-generated outputs thereby reducing distrust in AI-driven healthcare.