Journal of Clinical Pathology最新文献

Aims: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes delays and often results in over-referral of benign cases. We aimed to develop an automated triage system using artificial intelligence (AI) to enable more accurate and rapid referral of cases, thereby addressing these issues.

Methods: A retrospective dataset of H&E-stained whole slide images (WSI) of lymph nodes was taken from Newcastle University Hospital (302 cases) and Manchester Royal Infirmary Hospital (339 cases) with approximately equal representation of the 3 most prevalent lymphoma subtypes: follicular lymphoma, diffuse large B-cell and classic Hodgkin's lymphoma, as well as reactive controls. A subset (80%) of the data was used for training, a further validation subset (10%) for model selection and a final non-overlapping test subset (10%) for clinical evaluation.

Results: AI triage achieved multiclass accuracy of 0.828±0.041 and overall accuracy of 0.932±0.024 when discriminating between reactive and malignant cases. Its ability to detect lymphoma was equivalent to that of two haematopathologists (0.925, 0.950) and higher than a non-specialist pathologist (0.75) repeating the same task. To aid explainability, the AI tool also provides uncertainty estimation and attention heatmaps.

Conclusions: Automated triage using AI holds great promise in contributing to the accurate and timely diagnosis of lymphoma, ultimately benefiting patient care and outcomes.

Aims: Adjuvant chemotherapy after radical cystectomy can reduce the risk of recurrence and death in advanced muscle-invasive urothelial bladder cancer (MIBC). Molecular subtypes have been shown to be associated with survival. However, their predictive value to guide treatment decisions is controversial and data to use subtypes as guidance for adjuvant chemotherapy is sparse. We aimed to assess survival rates based on MIBC consensus molecular subtypes with and without adjuvant chemotherapy.

Methods: Gene expression profiles of 143 patients with MIBC undergoing radical cystectomy were determined from formalin-fixed, paraffin-embedded specimen to assign consensus molecular subtypes. Expression of programmed cell death ligand-1 (PD-L1) and immune cell infiltration were determined using multiplex immunofluorescence. Matched-pair analysis was performed to evaluate the effect of adjuvant chemotherapy on overall survival (OS) for molecular subtypes applying Kaplan-Meier and Cox regression survival analyses.

Results: Samples were luminal papillary: 9.1% (n=13), luminal non-specified: 6.3% (n=9), luminal unstable: 4.9% (n=7), stroma-rich: 27.9% (n=40), basal/squamous (Ba/Sq): 48.9% (n=70) and neuroendocrine-like (NE-like): 2.8% (n=4). Ba/Sq tumours had the highest concentration of PD-L1+ tumour and immune cells. Patients with luminal subtypes had better OS than those with NE-like (HR 0.2, 95% CI 0.1 to 0.7, p<0.05) and Ba/Sq (HR 0.5, 95% CI 0.2 to 0.9, p<0.05). No survival benefit with adjuvant chemotherapy was observed for luminal tumours, whereas Ba/Sq had significantly improved survival rates with adjuvant chemotherapy. Retrospective design and sample size are the main limitations.

Conclusion: Consensus molecular subtypes can be used to stratify patients with MIBC. Luminal tumours have the best prognosis and less benefit when receiving adjuvant chemotherapy compared with Ba/Sq tumours.

The most recent WHO classification of endocrine and neuroendocrine tumours has brought about significant changes in the diagnosis and grading of these lesions. For instance, pathologists now have the ability to stratify subsets of thyroid and adrenal neoplasms using various histological features and composite risk assessment models. Moreover, novel recommendations on how to approach endocrine neoplasia involve additional immunohistochemical analyses, and the recognition and implementation of these key markers is essential for modernising diagnostic capabilities. Additionally, an improved understanding of tumour origin has led to the renaming of several entities, resulting in the emergence of terminology not yet universally recognised. The adjustments in nomenclature and prognostication may pose a challenge for the clinical team, and care providers might be eager to engage in a dialogue with the diagnosing pathologist, as treatment guidelines have not fully caught up with these recent changes. Therefore, it is crucial for a surgical pathologist to be aware of the knowledge behind the implementation of changes in the WHO classification scheme. This review article will delve into the most significant diagnostic and prognostic changes related to lesions in the parathyroid, thyroid, adrenal glands and the gastroenteropancreatic neuroendocrine system. Additionally, the author will briefly share his personal reflections on the clinical implementation, drawing from a couple of years of experience with these new algorithms.

Aims: A new molecular subtype classification was proposed for small-cell lung carcinoma (SCLC). We aimed to further validate the classification in various SCLC patient samples using immunohistochemistry (IHC) to highlight its clinical significance.

Methods: We analysed the protein expression of four subtype (achaete-scute family BHLH transcription factor 1 (ASCL1), neuronal differentiation 1 (NEUROD1), POU class 2 homeobox 3 (POU2F3) and Yes1-associated transcriptional regulator (YAP1)) and two predictive markers (delta-like ligand 3 (DLL3) and MYC) using IHC in 216 specimens from 195 SCLC patients, including 21 pairs of resected biopsy tumours. Associations among molecular subtypes, clinicopathological features and prognostic implications were also explored.

Results: The ASCL1, NEUROD1, POU2F3, YAP1, DLL3 and MYC-positive expression rates were 70.3%, 56.9%, 14.9%, 19.0%, 75.4% and 22.6%, respectively. DLL3 expression had positive and negative associations with that of ASCL1 and POU2F3/YAP1, respectively, whereas MYC had the opposite effect. Strong associations of ASCL1 (Ρ=0.8603, p<0.0001), NEUROD1 (Ρ=0.8326, p<0.0001), POU2F3 (Ρ=0.6950, p<0.0001) and YAP1 (Ρ=0.7466, p<0.0001) expressions were detected between paired resected biopsy tumours. In addition to SCLC-A (ASCL1-dominant), SCLC-N (NEUROD1-dominant) and SCLC-P (POU2F3-dominant), unsupervised hierarchical cluster analyses identified a fourth, quadruple-negative SCLC subtype (SCLC-QN) characterised by the low expression of all four subtype-specific proteins, and 55.4% (n=108), 27.2% (n=53), 11.8% (n=23) and 5.6% (n=11) were categorised as SCLC-A, SCLC-N, SCLC-P and SCLC-QN, respectively. Significant enrichment of SCLC-P in the combined SCLC cohort was observed, and adenocarcinoma was more prevalent in SCLC-A, while large-cell neuroendocrine carcinoma was more commonly seen in SCLC-P. No survival difference was found among molecular subtypes.

Conclusions: Our results provide clinical insights into the diagnostic, prognostic and predictive significance of SCLC molecular subtype classifications.

Aims: Light chain proximal tubulopathy (LCPT) is a rare complication of paraprotein-related diseases. We report a case series to present the clinicopathological characteristics and outcomes of LCPT.

Methods: A multicentre retrospective case series of 47 patients with LCPT, consisting of 36 crystalline, three non-crystalline, and eight mixed LCPTs, was studied between January 2007 and December 2023.

Results: The median age at diagnosis was 57 years. Presentations included proteinuria (100%), renal insufficiency (62%) and Fanconi syndrome (68%). The underlying haematological diagnoses were monoclonal gammopathy of renal significance in 81% and multiple myeloma in 19%. Monoclonal light chain (LC) was detected in all cases using serum/urine-free LC assays or immunofixation electrophoresis. Among 36 crystalline LCPTs, 34 were κ-restricted and 2 λ-restricted. Three non-crystalline LCPTs were all λ-restricted. In mixed LCPTs, seven were κ-restricted and one was λ-restricted. Notably, 66% frozen-section immunofluorescence failed to reveal restricted LC, requiring paraffin-immunofluorescence or immunoelectron microscopy. The appearance of inclusions displayed intraindividual homogeneity but interindividual heterogeneity in 42 patients and notable intraindividual heterogeneity in the remaining 5 patients. Haematological complete response, very good partial response and partial response occurred in 61%. Kidney function improved or remained stable in 84%, worsened in 8% and progressed to end-stage renal disease in 8%.

Conclusions: Proteinuria and kidney dysfunction are the most common but less-specific renal manifestations of LCPTs, with most featuring Fanconi syndrome. Crystalline LCPT, primarily associated with κ-LC, is the predominant form. Most inclusions displayed intraindividual homogeneity and interindividual heterogeneity by electron microscopy. Most achieved haematological responses and favourable renal outcomes.

Aims: Mutations affecting exon 3 of the β-catenin (CTNNB1) gene result in constitutive activation of WNT signalling and are a diagnostic hallmark of several tumour entities including desmoid-type fibromatosis. They also define clinically relevant tumour subtypes within certain entities, such as endometrioid carcinoma. In diagnostics, β-catenin immunohistochemistry is widely used as a surrogate for CTNNB1 mutations. Yet, it is often difficult to assess in practice, given that the characteristic nuclear translocation may be focal or hard to distinguish from the spillover of the normal membranous staining.

Methods: We therefore examined lymphoid enhancer-binding factor 1 (LEF1) immunostaining, a nuclear marker of WNT activation that serves as a potential surrogate for CTNNB1 mutations.

Results: In a cohort of endometrial carcinomas with known mutation status (n=130) LEF1 was 85% accurate in predicting CTNNB1 mutation status (64% sensitivity, 90% specificity) while β-catenin was 76% accurate (72% sensitivity; 77% specificity). Across a variety of entities characterised by CTNNB1 mutations as putative drivers, we found diffuse and strong expression of LEF1 in 77% of cases. LEF1 immunostaining proved easier to interpret than β-catenin immunostaining in 54% of cases, more difficult in 1% of cases and comparable in the remaining cases.

Conclusion: We conclude that LEF1 immunostaining is a useful surrogate marker for CTNNB1 mutations. It favourably complements β-catenin immunohistochemistry and outperforms the latter as a single marker.