American journal of clinical pathology最新文献

Objective: This study aimed to elucidate the effect of gastric Helicobacter pylori (HP) colonization on the duodenal mucosa, focusing on intraepithelial lymphocyte (IEL) numbers and localizations.

Methods: The paired gastric and duodenal tissues from 132 patients with celiac disease (CD) and 190 individuals without CD were examined. Gastric HP status (presence and intensity) was compared with IEL counts per 100 enterocytes (IEL/100), localizations (basal-apical), and endoscopic, serologic, and clinicopathologic parameters.

Results: H pylori was detected in 176 (54.7%) gastric tissues, and its presence did not significantly change the duodenal IEL/100 counts in either CD (P = .121) or non-CD (P = .400) cases. It was seen in older individuals (P = .003), and age was also associated with HP intensity (P = .027). In non-CD cases, duodenal intraepithelial lymphocytosis (DIL) in HP-positive and HP-negative samples was 37 (33.9%) and 31 (38.3%), respectively (P = .538). Although a slight increase was observed with sparse HP colonization (+), intense colonization (+++) was significantly associated with less scalloping (P = .037), lower IEL/100 (P = .003), and antiendomysial antibody IgA (P = .048). A similar pattern was also observed in tissue transglutaminase IgA titers (P = .053).

Conclusions: Considering the effect of gastric HP on duodenal IELs, endoscopic and serologic parameters, depending on its intensity, will provide a more accurate estimation in cases where the cause of DIL is investigated.

Objective: TP53 mutations, including missense and inactivating (frameshift, splice site, and nonsense) mutations, occur in approximately 10% of myeloid neoplasms and confer adverse outcomes. Classification of myeloid neoplasms by World Health Organization and International Consensus Classification standards recognizes the importance of early detection of TP53 mutations. p53 immunohistochemistry (IHC) is a widely accessible method used to detect mutations; however, previous studies have demonstrated variable accuracy, especially for inactivating TP53 mutations. Recently, sequencing using targeted panels has seen increased use. Although highly accurate, sequencing is resource intensive and not universally available.

Methods: Using 134 bone marrow samples from patients with acute myeloid leukemia evaluated for TP53 mutation by sequencing, we assessed the concordance of p53 IHC with sequencing as well as the interrater-reliability for IHC intensity and percent positivity.

Results: Consistent with previous studies, we found that p53 IHC was strongly specific and modestly sensitive for missense mutations and that overall performance improved with dedicated hematopathology training. We also found that IHC performed poorly for inactivating mutations and was even variable between cases harboring identical amino acid changes. Low predicted transcriptional activity of p53 missense proteins correlated with a mutant pattern of IHC staining. The status of the second allele and variant allele frequency also affected the accuracy of p53 IHC as a surrogate for TP53 allele status.

Conclusion: Cases of acute myeloid leukemia with TP53 mutations predicted to have low transcriptional activity showed reduced overall survival. Our results demonstrate limited practical utility of p53 IHC for accurate evaluation of TP53 mutation status because of multifactorial confounders.

Objective: This study investigates the academic backgrounds and medical school pathology exposure among first-year pathology residents, comparing graduates from the United States and international medical schools.

Methods: A survey was administered as part of the Resident In-Service Examination First, offered by the American Society for Clinical Pathology, which assessed academic background, preparedness for residency, and prior exposure to pathology education. Associations between undergraduate pathology exposure, timing of residency selection, reported preparedness, and examination performance were analyzed.

Results: Of the 417 residents who completed the survey, 39.3% had graduated from international medical institutions. International medical graduates reported higher rates of medical school curricula that included required pathology rotations (33.5% vs 3.6%, P = .001) and greater perceived preparedness for anatomic pathology residency (28.7% vs 15.8%, P = .002), with no significant difference in examination performance. Additionally, 22.5% of US medical student respondents selected pathology before medical school, compared to only 10.4% of international medical graduates (P = .002).

Conclusions: This study highlights differences in educational exposure and perceived preparedness for pathology residency between US and international medical graduates, with international medical graduates reporting more preresidency exposure to pathology and higher perceived confidence at the start of residency. These findings suggest potential areas for curricular improvement in US medical schools to enhance pathology exposure.

Objective: We sought to establish the US Department of Veterans Affairs (VA) Veterans Health Administration (VHA) Sequencing for Research Clinical and Epidemiology (SeqFORCE) multilaboratory consortium for SARS-CoV-2 whole-genome sequencing (WGS).

Methods: Clinical criteria were established for sending patient and employee samples from 145 VHA medical centers to 10 VHA clinical laboratories using 4 different WGS platforms. A linked pipeline among laboratories for SARS-CoV-2 clade and lineage interpretation, result transmission to electronic health records, and data storage was developed.

Results: The SeqFORCE program went live on July 1, 2021. As of December 15, 2024, 51 307 samples have been analyzed by WGS for SARS-CoV-2. The median participant age was 60 years, 76.6% were male, and 13.5% were inpatients; 96.5% were Delta, Omicron, and Recombinant sublineages; and 78.5% represented SARS-CoV-2 postvaccination samples among patients and staff.

Conclusions: Establishment of VA SeqFORCE enabled national population analysis for use in epidemiologic response and population health policy as well as expanded SARS-CoV-2 sequencing capacity to meet the demand for clinical and public health sequencing. The program consolidated operations using standardized procedures, test setup, analysis, reporting, and tracking. It also improved oversight and governance of VA contributions to global databases, mitigated system inefficiencies, and prepared VHA for future genomic challenges.

Objective: Cytologic examination, which confirms the presence or absence of malignant cells, detects malignant cells from various organs, with adenocarcinoma as the most common histologic type. We developed a deep learning model to detect malignant cells in images obtained following effusion cytology.

Methods: The deep learning model was created using the YOLOv8 object detection algorithm (Roboflow, Inc) and 275 cases of adenocarcinoma comprising 12 182 images and 29 245 labels as well as 188 cases negative for malignancy comprising 1980 images.

Results: The adenocarcinoma test dataset exhibited Precision, Recall, F1, and mean average Precision scores of 0.909, 0.911, 0.910, and 0.955, respectively. The number of adenocarcinoma test images in which 1 or more malignant cells were detected was 2710 of 2731. The sensitivity in the nonadenocarcinoma dataset was 97.1%, and the false-positive rate in the negative-for-malignancy dataset was 7.3%. The accuracy, sensitivity, and specificity of the model using all the test datasets were 96.3%, 98.5%, and 92.7%, respectively.

Conclusions: Although some issues regarding cell annotation when creating an object detection model remain, the accuracy is sufficient to assist cancer screening in effusion cytology. It is vital to reliably detect malignant cells in effusion cytology, and the further development of automated systems to reduce false-negative results is expected.

Objective: Rare large B-cell lymphomas (LBCLs) present with concurrent or subsequent lymphomatous effusion (solid-effusion LBCL, SE-LBCL), which may have an inferior prognosis compared with their noneffusion counterpart. In addition, the relationship between SE-LBCL and human herpesvirus 8-negative effusion-based LBCL (EB-LBCL) remains unclear.

Methods: We collected 141 cases of SE-LBCL and a control cohort of 101 cases of stage IV solid-only LBCL. The clinicopathologic features were analyzed and compared between SE-LBCL and solid-only LBCL.

Results: Patients with SE-LBCL had a median age of 67 years with a male-to-female ratio of 1.3:1. Eighty-six patients had concurrent solid lymphoma and lymphomatous effusion, whereas 55 developed lymphomatous effusion subsequently. Most cases involved the pleural cavities (79%, 112/141), followed by the peritoneal (21%, 29/141) and pericardial (11%, 16/141) cavities. BCL6, CD10, and MUM1 were expressed in 77% (90/117), 46% (60/130), and 61% (58/95) of cases, respectively, and 58% (71/123) were subclassified into the germinal center B-cell (GCB) subtype. Rearrangements of BCL2, BCL6, and MYC were detected in 42% (31/73), 35% (22/63), and 40% (35/88), respectively, and 22% (19/87) had both MYC and BCL2 rearrangements. The patients with SE-LBCL had a dismal prognosis, with a median survival of 5.7 months, which was significantly worse than solid-only LBCL (147.5 months; P < .0001).

Conclusions: The pathologic features of SE-LBCL were similar to those of solid-only LBCL but distinct from those of EB-LBCL; in particular, lymphomatous effusion was an independently adverse prognostic factor in LBCL. Our study underscores the need for surveillance of lymphomatous effusion during LBCL staging and development of effective therapeutic regimens for SE-LBCL.

Objective: Despite exponential growth in artificial intelligence (AI) research for laboratory medicine and pathology, a significant gap exists between model development and clinical AI implementation. This article introduces a structured framework, the Clinical AI Readiness Evaluator (CARE), to bridge this gap and support the responsible adoption of AI in clinical laboratory settings.

Methods: Building upon the Machine Learning Technology Readiness Levels framework, we developed CARE specifically for the clinical laboratory environment by incorporating health care-specific requirements, regulatory considerations, and workflow integration needs. This framework was iteratively refined through practical application across diverse AI use cases within laboratory medicine and pathology.

Results: The CARE framework provides a systematic approach to AI development and implementation through 8 component workstreams: clinical use case, data, data pipeline, code, clinical user experience, clinical technology infrastructure, clinical orchestration, and regulatory compliance. Unlike generic AI frameworks, CARE distinguishes itself by emphasizing both health care and laboratory workflow integration, regulatory requirements, ethical considerations, and comprehensive validation for clinical contexts. The framework accommodates both internally developed models and commercial AI solutions, providing clear guidance through technology readiness levels and structured review processes.

Conclusions: The CARE framework addresses the unique challenges of implementing AI in laboratory medicine and pathology by providing a comprehensive roadmap from initial concepts through clinical deployment and maintenance. This article, the first in a series of 4, establishes the foundational AI lifecycle framework, while subsequent articles will explore data documentation, ethical AI considerations, and governance structures. By adopting this structured approach, laboratories can responsibly harness AI's potential to enhance diagnostic accuracy and operational efficiencies and, ultimately, improve patient care.

Objective: Prior studies have reported the loss of α-smooth muscle actin (α-SMA) immunoreactivity in the inner circular layer of the muscularis propria in small bowel motility disorder cases, but this remains controversial with conflicting data. In this study, we aimed to characterize α-SMA immunoreactivity in the muscularis propria of the small intestine-specifically, jejunum-in patients with and without small bowel motility disorder.

Methods: A total of 28 transmural proximal jejunum biopsy specimens from adult patients with clinical impression of upper gastrointestinal dysmotility disorder and 64 control tissues were evaluated. The controls were full-thickness, longitudinal tissue sections from segmental resections performed due to gunshot wounds, multivisceral transplant donation, and tumors. Immunostaining for α-SMA was performed with appropriate controls to confirm the presence of immunoreactivity in the circular and longitudinal muscle layers of the muscularis propria in each sample and recorded as retained or diminished.

Results: In the small bowel motility disorder and control cases, 42.9% (12/28) and 70.3% (49/64) of the cases showed no or minimal α-SMA immunoreactivity in the inner circular layer with peripheral accentuation, respectively.

Conclusions: Loss or diminished α-SMA immunoreactivity in the inner circular layer of the muscularis propria occurs with a similar frequency in cases with and without small bowel motility disorder and does not correlate with impairment of function.

Objective: Neurogenic differentiation factor 1 (NeuroD1) is a known marker of a subtype of small cell lung carcinoma (SCLC). In this study, we aim to assess whether there is an association between NeuroD1 with Merkel cell polyomavirus (MCPyV) status, keratin 20, thyroid transcription factor 1 (TTF1), and overall survival (OS) in 125 Merkel cell carcinomas (MCCs).

Methods: NeuroD1-positive MCC tumors were characterized by immunohistochemical stains and an external RNA sequencing data set.

Results: NeuroD1 positivity (10%-100%) was seen in 29 (23%) of 125 cases, with 60 (48%) of 126 and 113 (94%) of 120 tumors MCPyV positive and keratin 20 positive, respectively. Focal TTF1 expression was seen in 9 (7.5%) of 120 tumors. NeuroD1 expression was seen more frequently in MCPyV-negative than MCPyV-positive MCCs (P = .0002) and more frequently in keratin 20-negative tumors vs keratin 20-positive ones (P < .0001). Increased NEUROD1 expression in MCPyV-negative MCC (P < .005) was confirmed in an external RNA sequencing data set (GSE235092). Univariate analyses showed NeuroD1 positivity and MCPyV-negative status correlated with worse OS (P = .024 and P = .0076, respectively); however, only MCPyV status remained significant in multivariate analyses (P = .033).

Conclusions: NeuroD1-positive MCCs are significantly correlated with MCPyV-negative, keratin 20-negative expression, and focal TTF1 expression. NeuroD1 expression can pose a potential diagnostic pitfall in the distinction of MCC from SCLC, especially in a setting of a limited immunohistochemical panel.

Objective: Drug interference with pretransfusion testing in patients with multiple myeloma (MM) treated with daratumumab (DARA) is well recognized. Current guidelines recommend that these patients should have a red blood cell (RBC) phenotype or genotype before DARA initiation; however, there are no other standards for pretransfusion testing. While prior publications report mitigation strategies and low RBC alloimmunization risk, they do not propose a cost-effective strategy for pretransfusion testing. This study aims to assess the RBC alloimmunization risk in patients treated with DARA and propose a cost-effective algorithm for pretransfusion testing.

Methods: This is a retrospective study of patients treated with DARA and receiving RBC transfusions over 9.4 years (October 1, 2015, to January 30, 2025). Demographic data; a complete serologic profile, including blood typing, antibody screen (Ab screen), and antibody identification (Ab ID); RBC phenotype/genotype; and crossmatch data were collected for each patient. The clinically significant antibody formation incidence was recorded pre- and post-DARA and compared with a control group, with statistical significance defined as P < .05. The mitigation strategy initially used for pretransfusion testing and its optimized version are described along with their cost.

Results: Of the 850 patients with MM on DARA therapy who were identified, 172 (20%) received at least 1 RBC transfusion. Ab screens were performed on all patients pre- and post-DARA therapy. Following drug administration, all patients showed a panagglutinin, and no patients formed new clinically significant alloantibodies. The turnaround time (TAT) and cost significantly decreased when the pretransfusion strategy with optimizing pretransfusion strategy.

Conclusions: This is the most extensive study on patients treated with DARA and transfused, demonstrating no increased alloimmunization risk. DARA-related transfusion interference may be successfully mitigated by the novel strategy proposed here.