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Clinics in Laboratory Medicine Dec 2023The diagnosis of myelodysplastic syndromes/neoplasms (MDS) has evolved over the years with the incorporation of genetic abnormalities to establish a diagnosis, their... (Review)
Review
The diagnosis of myelodysplastic syndromes/neoplasms (MDS) has evolved over the years with the incorporation of genetic abnormalities to establish a diagnosis, their impact on risk stratification, prognostication, and therapeutic options. Hematopathologists are the cornerstone to establish an accurate diagnosis and ensure patients receive the best available treatment option. Hematopathologists and clinicians must work closely together to establish the best disease subclassification, by combining pathologic findings with the clinical presentation. This will ensure patients receive the best therapeutic approach by better understanding the disease entity. In this review, we discuss how we approach a bone marrow biopsy report in the management of MDS.
Topics: Humans; Prognosis; Pathologists; Neoplasms; Myelodysplastic Syndromes
PubMed: 37865511
DOI: 10.1016/j.cll.2023.07.003 -
Histopathology Mar 2024In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the... (Review)
Review
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
Topics: Humans; Pathologists; Paraffin Embedding; Gene Expression Profiling; Transcriptome; Formaldehyde
PubMed: 37991396
DOI: 10.1111/his.15093 -
Annals of Clinical and Laboratory... Nov 2023To evaluate the utility of artificial intelligence-powered language models (ChatGPT 3.5 and GPT-4) compared to trainees and clinical chemists in responding to common...
OBJECTIVE
To evaluate the utility of artificial intelligence-powered language models (ChatGPT 3.5 and GPT-4) compared to trainees and clinical chemists in responding to common laboratory questions in the broad area of Clinical Chemistry.
METHODS
35 questions from real-life case scenarios, clinical consultations, and clinical chemistry testing questions were used to evaluate ChatGPT 3.5, and GPT-4 alongside clinical chemistry trainees (residents/fellows) and clinical chemistry faculty. The responses were scored based on category and based on years of experience.
RESULTS
The Senior Chemistry Faculty demonstrated superior accuracy with 100% of correct responses compared to 90.5%, 82.9%, and 71.4% of correct responses from the junior chemistry faculty, fellows, and residents respectively. They all outperformed both ChatGPT 3.5 and GPT-4 which generated 60% and 71.4% correct responses respectively. Of the sub-categories examined, ChatGPT 3.5 achieved 100% accuracy in endocrinology while GPT-4 did not achieve 100% accuracy in any subcategory. GPT-4 was overall better than ChatGPT 3.5 by generating similar correct responses as residents (71.4%) but performed poorly to human participants when both partially correct and incorrect indices were considered.
CONCLUSION
Despite all the advances in AI-powered language models, ChatGPT 3.5 and GPT-4 cannot replace a trained pathologist in answering clinical chemistry questions. Caution should be observed by people, especially those not trained in clinical chemistry, to interpret test results using chatbots.
Topics: Humans; Chemistry, Clinical; Artificial Intelligence; Laboratories; Pathologists
PubMed: 38182139
DOI: No ID Found -
Fetal and Pediatric Pathology Dec 2023Costs for sendout genetic testing on in-patients are billed to the hospital. Turnaround times are several weeks, often extending past the inpatient hospitalization. We...
Costs for sendout genetic testing on in-patients are billed to the hospital. Turnaround times are several weeks, often extending past the inpatient hospitalization. We concurrently reviewed all sendout genetic in-patient test requests over an 18-month period, deferring those that could be obtained as an outpatient, directing the tests to less expensive laboratories with complementary testing profiles, and identifying no-charge sponsored tests. Of 121 test requests, 25 were deferred, alternative less expensive laboratories were identified for 8, 16 requests were directed to sponsored tests, for a 42.3% cost saving. Of the 96 tests sent, 18 (18.8%) identified an explanatory genetic abnormality. Approximately 40% of the sendout genetic testing costs were reduced with prior test review. Deferment, alternative laboratories, and sponsored tests contributed to cost savings. Efficiency of diagnostic inpatient genetic testing was approximately 20%.
Topics: Child; Humans; Genetic Testing; Hospitalization; Laboratories; Pathologists; Pediatrics; Inpatients
PubMed: 37787107
DOI: 10.1080/15513815.2023.2263790 -
Advances in Anatomic Pathology Jan 2024The classification of thymoma continues to be a source of controversy in pathology. The difficulties in histologic classification are evident from the number of...
The classification of thymoma continues to be a source of controversy in pathology. The difficulties in histologic classification are evident from the number of proposals that have been offered over the years, as well as for the continuous changes and modifications introduced by the World Health Organization to their classification system over the past 20 years. We analyze here some of the issues involved in the classification of these tumors and the difficulties encountered for practicing pathologists in deciphering the "letters and numbers" system devised by the World Health Organization. We would like to propose an alternate approach to thymoma histologic classification that capitalizes on the basic observation of their cytologic features and incorporates the pattern of growth resulting from the interplay of the tumor cells with other cellular constituents as a secondary characteristic. The proposed histologic classification provides a simplified, reproducible means of histologically categorizing these tumors and can be easily understood by most practicing pathologists in simple and clear morphologic terms.
Topics: Humans; Thymoma; Prognosis; Thymus Neoplasms; World Health Organization; Pathologists
PubMed: 37702296
DOI: 10.1097/PAP.0000000000000412 -
Critical Reviews in Oncology/hematology Oct 2023Pembrolizumab has received approval as a first-line treatment for unresectable/metastatic triple-negative breast cancer (mTNBC) with a PD-L1 combined positive score... (Review)
Review
Pembrolizumab has received approval as a first-line treatment for unresectable/metastatic triple-negative breast cancer (mTNBC) with a PD-L1 combined positive score (CPS) of ≥ 10. However, assessing CPS in mTNBC poses challenges. Firstly, it represents a novel analysis for breast pathologists. Secondly, the heterogeneity of PD-L1 expression in mTNBC further complicates the assessment. Lastly, the lack of standardized assays and staining platforms adds to the complexity. In KEYNOTE trials, PD-L1 expression was evaluated using the IHC 22C3 pharmDx kit as a companion diagnostic test. However, both the 22C3 pharmDx and VENTANA PD-L1 (SP263) assays are validated for CPS assessment. Consequently, assay-platform choice, staining conditions, and scoring methods can significantly impact the testing outcomes. This consensus paper aims to discuss the intricacies of PD-L1 CPS testing in mTNBC and provide practical recommendations for pathologists. Additionally, we present findings from a nationwide Italian survey elucidating the state-of-the-art in PD-L1 CPS testing in mTNBC.
Topics: Humans; B7-H1 Antigen; Pathologists; Triple Negative Breast Neoplasms; Breast; Consensus
PubMed: 37595344
DOI: 10.1016/j.critrevonc.2023.104103 -
Cancers Sep 2023With the rise of novel immunotherapies able to stimulate the antitumor immune response, increasing literature concerning the immunogenicity of breast cancer has been... (Review)
Review
With the rise of novel immunotherapies able to stimulate the antitumor immune response, increasing literature concerning the immunogenicity of breast cancer has been published in recent years. Numerous clinical studies have been conducted in order to identify novel biomarkers that could reflect the immunogenicity of BC and predict response to immunotherapy. In this regard, TILs have emerged as an important immunological biomarker related to the antitumor immune response in BC. TILs are more frequently observed in triple-negative breast cancer and HER2+ subtypes, where increased TIL levels have been linked to a better response to neoadjuvant chemotherapy and improved survival. PD-L1 is a type 1 transmembrane protein ligand expressed on T lymphocytes, B lymphocytes, and antigen-presenting cells and is considered a key inhibitory checkpoint involved in cancer immune regulation. PD-L1 immunohistochemical expression in breast cancer is observed in about 10-30% of cases and is extremely variable based on tumor stage and molecular subtypes. Briefly, TNBC shows the highest percentage of PD-L1 positivity, followed by HER2+ tumors. On the other hand, PD-L1 is rarely expressed (0-10% of cases) in hormone-receptor-positive BC. The prognostic role of PD-L1 expression in BC is still controversial since different immunohistochemistry (IHC) clones, cut-off points, and scoring systems have been utilized across published studies. In the present paper, an extensive review of the current knowledge of the immune landscape of BC is provided. TILS and PD-L1 expression across different BC subtypes are discussed, providing a guide for their pathological assessment and reporting.
PubMed: 37760449
DOI: 10.3390/cancers15184479 -
Gut Sep 2023To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early...
OBJECTIVE
To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.
DESIGN
A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site.
RESULTS
Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features.
CONCLUSION
The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption.
Topics: Humans; Male; Female; Artificial Intelligence; Retrospective Studies; State Medicine; Algorithms; Biopsy
PubMed: 37173125
DOI: 10.1136/gutjnl-2023-329512 -
Modern Pathology : An Official Journal... Nov 2023Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens....
Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens. Major pathological response (MPR) has recently been used for treatment evaluation and as an economical survival surrogate; however, interobserver variability and poor reproducibility are often noted. The aim of this study was to develop a deep learning (DL) model to predict MPR from hematoxylin and eosin-stained tissue images and to validate its utility for clinical use. We collected data on 125 primary non-small cell lung carcinoma cases that were resected after neoadjuvant therapy. The cases were randomly divided into 55 for training/validation and 70 for testing. A total of 261 hematoxylin and eosin-stained slides were obtained from the maximum tumor beds, and whole slide images were prepared. We used a multiscale patch model that can adaptively weight multiple convolutional neural networks trained with different field-of-view images. We performed 3-fold cross-validation to evaluate the model. During testing, we compared the percentages of viable tumor evaluated by annotator pathologists (reviewed data), those evaluated by nonannotator pathologists (primary data), and those predicted by the DL-based model using 2-class confusion matrices and receiver operating characteristic curves and performed a survival analysis between MPR-achieved and non-MPR cases. In cross-validation, accuracy and mean F1 score were 0.859 and 0.805, respectively. During testing, accuracy and mean F1 score with reviewed data and those with primary data were 0.986, 0.985, 0.943, and 0.943, respectively. The areas under the receiver operating characteristic curve with reviewed and primary data were 0.999 and 0.978, respectively. The disease-free survival of MPR-achieved cases with reviewed and primary data was significantly better than that of the non-MPR cases (P<.001 and P=.001), and that predicted by the DL-based model was almost identical (P=.005). The DL model may support pathologist evaluations and can offer accurate determinations of MPR in patients.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Neoadjuvant Therapy; Deep Learning; Eosine Yellowish-(YS); Hematoxylin; Reproducibility of Results; Lung Neoplasms
PubMed: 37580019
DOI: 10.1016/j.modpat.2023.100302 -
Journal of Pathology Informatics 2023Our objective was to develop an automated deep-learning-based method to evaluate cellularity in rat bone marrow hematoxylin and eosin whole slide images for preclinical...
Our objective was to develop an automated deep-learning-based method to evaluate cellularity in rat bone marrow hematoxylin and eosin whole slide images for preclinical safety assessment. We trained a shallow CNN for segmenting marrow, 2 Mask R-CNN models for segmenting megakaryocytes (MKCs), and small hematopoietic cells (SHCs), and a SegNet model for segmenting red blood cells. We incorporated the models into a pipeline that identifies and counts MKCs and SHCs in rat bone marrow. We compared cell segmentation and counts that our method generated to those that pathologists generated on 10 slides with a range of cell depletion levels from 10 studies. For SHCs, we compared cell counts that our method generated to counts generated by Cellpose and Stardist. The median Dice and object Dice scores for MKCs using our method vs pathologist consensus and the inter- and intra-pathologist variation were comparable, with overlapping first-third quartile ranges. For SHCs, the median scores were close, with first-third quartile ranges partially overlapping intra-pathologist variation. For SHCs, in comparison to Cellpose and Stardist, counts from our method were closer to pathologist counts, with a smaller 95% limits of agreement range. The performance of the bone marrow analysis pipeline supports its incorporation into routine use as an aid for hematotoxicity assessment by pathologists. The pipeline could help expedite hematotoxicity assessment in preclinical studies and consequently could expedite drug development. The method may enable meta-analysis of rat bone marrow characteristics from future and historical whole slide images and may generate new biological insights from cross-study comparisons.
PubMed: 37743975
DOI: 10.1016/j.jpi.2023.100333