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Journal of the American Veterinary... Dec 2023To conduct a retrospective multi-institutional study reporting short- and long-term outcomes of adrenalectomy in patients presenting with acute hemorrhage secondary to...
Adrenal tumors treated by adrenalectomy following spontaneous rupture carry an overall favorable prognosis: retrospective evaluation of outcomes in 59 dogs and 3 cats (2000-2021).
OBJECTIVE
To conduct a retrospective multi-institutional study reporting short- and long-term outcomes of adrenalectomy in patients presenting with acute hemorrhage secondary to spontaneous adrenal rupture.
ANIMALS
59 dogs and 3 cats.
METHODS
Medical records of dogs and cats undergoing adrenalectomy between 2000 and 2021 for ruptured adrenal masses were reviewed. Data collected included clinical presentation, preoperative diagnostics, surgical report, anesthesia and hospitalization findings, histopathology, adjuvant treatments, and long-term outcome (recurrence, metastasis, and survival).
RESULTS
Median time from hospital admission to surgery was 3 days, with 34% of surgeries being performed emergently (within 1 day of presentation). Need for intraoperative blood transfusion was significantly associated with emergent surgery and presence of active intraoperative hemorrhage. The short-term (≤ 14 days) complication and mortality rates were 42% and 21%, respectively. Negative prognostic factors for short-term survival included emergent surgery, intraoperative hypotension, and performing additional surgical procedures. Diagnoses included adrenocortical neoplasia (malignant [41%], benign [12%], and undetermined [5%]), pheochromocytoma (38%), a single case of adrenal fibrosis and hemorrhage (2%), and a single case of hemangiosarcoma (2%). Local recurrence and metastasis of adrenocortical carcinoma were confirmed in 1 and 3 cases, respectively. Overall median survival time was 574 days and 900 days when short-term mortality was censored. No significant relationship was found between histopathological diagnosis and survival.
CLINICAL RELEVANCE
Adrenalectomy for ruptured adrenal gland masses was associated with similar short- and long-term outcomes as compared with previously reported nonruptured cases. If hemodynamic stability can be achieved, delaying surgery and limiting additional procedures appear indicated to optimize short-term survival.
Topics: Animals; Cats; Dogs; Humans; Adrenal Cortex Neoplasms; Adrenal Gland Neoplasms; Adrenalectomy; Cat Diseases; Dog Diseases; Hemorrhage; Laparoscopy; Retrospective Studies; Rupture, Spontaneous; Treatment Outcome
PubMed: 37734721
DOI: 10.2460/javma.23.06.0324 -
Histopathology Sep 2023Cribriform architecture has been recognised as an independent parameter for prostate cancer outcome. Little is yet known on the added value of individual Gleason 5... (Review)
Review
Cribriform architecture has been recognised as an independent parameter for prostate cancer outcome. Little is yet known on the added value of individual Gleason 5 growth patterns. Comedonecrosis is assigned Gleason pattern 5 and can occur in both invasive and intraductal carcinoma. The aim of this study is to systematically review the literature for the prognostic value of comedonecrosis in prostate cancer. A systematic literature search of Medline, Web of Science, Cochrane library and Google scholar was performed according to the Preferred reporting items for systematic reviews and meta-analysis (PRISMA)guidelines. After identification and screening of all relevant studies published up to July 2022, 12 manuscripts were included. Clinicopathological data were extracted and the presence of comedonecrosis in either invasive, intraductal or ductal carcinoma was associated with at least one clinical outcome measure. No meta-analysis was performed. Eight of 11 studies showed that comedonecrosis was significantly associated with biochemical recurrence and two studies with metastasis or death. The only studies using metastasis-free and disease specific-free survival as an endpoint both found comedonecrosis to be an independent prognostic parameter in multivariate analysis. The studies were all retrospective and demonstrated considerable heterogeneity with regard to clinical specimen, tumour type, grade group, correction for confounding factors and endpoints. This systematic review demonstrates weak evidence for comedonecrosis to be associated with adverse prostate cancer outcome. Study heterogeneity and lack of correction for confounding factors prohibit drawing of definitive conclusions.
Topics: Male; Humans; Retrospective Studies; Prostatic Neoplasms; Prognosis; Neoplasm Grading; Carcinoma, Ductal, Breast; Breast Neoplasms
PubMed: 37195595
DOI: 10.1111/his.14945 -
Cancer Biomarkers : Section a of... 2024Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast... (Review)
Review
BACKGROUND
Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.
OBJECTIVE
To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.
METHODS
This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models.
RESULTS
Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images.
CONCLUSION
This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.
Topics: Humans; Breast Neoplasms; Deep Learning; Female; Image Processing, Computer-Assisted; Algorithms; Image Interpretation, Computer-Assisted
PubMed: 38517775
DOI: 10.3233/CBM-230251 -
European Radiology Nov 2023To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected...
OBJECTIVES
To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application.
METHODS
This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS).
RESULTS
In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set.
CONCLUSIONS
The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes.
CLINICAL RELEVANCE STATEMENT
Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate.
KEY POINTS
• The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Retrospective Studies; Neoplasm Invasiveness; Tomography, X-Ray Computed
PubMed: 37338558
DOI: 10.1007/s00330-023-09852-1 -
Acta Oncologica (Stockholm, Sweden) Dec 2023Targeted second-look ultrasound (US) is often performed following MRI of the breast to determine if an MRI-detected lesion is visible on US and thus amenable to...
INTRODUCTION
Targeted second-look ultrasound (US) is often performed following MRI of the breast to determine if an MRI-detected lesion is visible on US and thus amenable to US-guided biopsy. This study aimed to assess the pathology of lesions detected and biopsied on the second-look US. In particular, for multifocal cancers, whether the pathology of additional lesions detected by second-look US is different to the index lesion.
METHODS
Multicentre single-institution retrospective study of 300 consecutive cases of second-look US biopsies from August 2017 to April 2022 was performed, with their histopathology and imaging characteristics recorded. For multifocal cancers, Wilcoxon Signed Ranks Tests were used to compare differences between the index and additional lesions in the histopathology category (i.e., high-risk benign, precursor or malignant) and BRE grade.
RESULTS
69 multifocal cancers were detected. For the purposes of this study, additional lesions were considered more invasive if they were of a higher histopathological category or BRE grade, or demonstrated lymphovascular invasion when the primary lesion did not. 15/69 additional lesions were not seen on the initial mammogram/tomography or ultrasound, seen on subsequent MRI and second look US, and were less invasive than the index lesion. 3/69 additional lesions were more invasive than their index lesions. Wilcoxon Signed Ranks test showed additional lesions were of either similar or lesser invasiveness compared to index lesions (z= -3.207, = 0.001) in the histopathological category, and the same or lower BRE grade (z= -2.972, = 0.003).
CONCLUSION
In multifocal breast cancers, additional lesions detected on MRI and second-look US have the same or less invasive histopathology compared to the index lesion.
Topics: Female; Humans; Breast Neoplasms; Ultrasonography, Mammary; Retrospective Studies; Breast; Magnetic Resonance Imaging; Sensitivity and Specificity
PubMed: 37890095
DOI: 10.1080/0284186X.2023.2273897 -
Scientific Reports Jan 2024A growing literature suggests that plasma levels of tau phosphorylated at amino acid 217 (pTau217) performs similarly to cerebrospinal fluid (CSF) biomarkers and PET...
A growing literature suggests that plasma levels of tau phosphorylated at amino acid 217 (pTau217) performs similarly to cerebrospinal fluid (CSF) biomarkers and PET imaging to detect amyloid pathology and to provide diagnostic and prognostic information in Alzheimer's disease (AD), but a significant limiting factor thus far has been a lack of widely available immunoassays. We evaluated a novel pTau217 S-PLEX® assay developed by Meso Scale Discovery (MSD; Rockville, MD) in plasma from 131 individuals with AD confirmed by CSF biomarkers and controls. Technical performance of the assay was excellent with an LLOQ of 1.84 pg/mL and intra/interplate CVs of 5.5% (0.3-15.0%) and 5.7% (range 0.3-13.4%), respectively. The pTau217 plasma assay differentiated AD and controls with an AUC of 0.98 (95% CI 0.96-1.0) and pTau217 levels were 3.9-fold higher in individuals with AD. This performance was significantly better than what was observed for plasma pTau181, performed in parallel, and comparable to published data on existing pTau217 assays. While further clinical validation and head-to-head comparisons are needed to fully establish the role for the novel pTau217 S-PLEX assay, these data demonstrate the utility of the assay to detect AD pathology.
Topics: Humans; Alzheimer Disease; Immunologic Tests; Amino Acids; Amyloidogenic Proteins; Biomarkers
PubMed: 38182740
DOI: 10.1038/s41598-024-51334-x -
BMC Neurology Aug 2023Autism spectrum disorder (ASD) affects 1 in 100 children globally with a rapidly increasing prevalence. To the best of our knowledge, no data exists on the genetic...
BACKGROUND
Autism spectrum disorder (ASD) affects 1 in 100 children globally with a rapidly increasing prevalence. To the best of our knowledge, no data exists on the genetic architecture of ASD in India. This study aimed to identify the genetic architecture of ASD in India and to assess the use of whole exome sequencing (WES) as a first-tier test instead of chromosomal microarray (CMA) for genetic diagnosis.
METHODS
Between 2020 and 2022, 101 patient-parent trios of Indian origin diagnosed with ASD according to the Diagnostic and Statistical Manual, 5th edition, were recruited. All probands underwent a sequential genetic testing pathway consisting of karyotyping, Fragile-X testing (in male probands only), CMA and WES. Candidate variant validation and parental segregation analysis was performed using orthogonal methods.
RESULTS
Of 101 trios, no probands were identified with a gross chromosomal anomaly or Fragile-X. Three (2.9%) and 30 (29.7%) trios received a confirmed genetic diagnosis from CMA and WES, respectively. Amongst diagnosis from WES, SNVs were detected in 27 cases (90%) and CNVs in 3 cases (10%), including the 3 CNVs detected from CMA. Segregation analysis showed 66.6% (n = 3 for CNVs and n = 17 for SNVs) and 16.6% (n = 5) of the cases had de novo and recessive variants respectively, which is in concordance with the distribution of variant types and mode of inheritance observed in ASD patients of non-Hispanic white/ European ethnicity. MECP2 gene was the most recurrently mutated gene (n = 6; 20%) in the present cohort. Majority of the affected genes identified in the study cohort are involved in synaptic formation, transcription and its regulation, ubiquitination and chromatin remodeling.
CONCLUSIONS
Our study suggests de novo variants as a major cause of ASD in the Indian population, with Rett syndrome as the most commonly detected disorder. Furthermore, we provide evidence of a significant difference in the diagnostic yield between CMA (3%) and WES (30%) which supports the implementation of WES as a first-tier test for genetic diagnosis of ASD in India.
Topics: Child; Humans; Male; Autism Spectrum Disorder; Exome Sequencing; Pathology, Molecular; Genetic Testing; Microarray Analysis
PubMed: 37543562
DOI: 10.1186/s12883-023-03341-0 -
European Urology Oncology Jun 2024Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to... (Review)
Review
CONTEXT
Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases.
OBJECTIVE
To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).
EVIDENCE ACQUISITION
A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.
EVIDENCE SYNTHESIS
In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported.
CONCLUSIONS
This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice.
PATIENT SUMMARY
Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.
Topics: Humans; Carcinoma, Renal Cell; Kidney Neoplasms; Pathology, Clinical; Computational Biology; Machine Learning
PubMed: 37925349
DOI: 10.1016/j.euo.2023.10.018 -
Journal of Cancer Research and Clinical... Dec 2023We evaluated the current performance of diagnostic ultrasound (US) for detecting cervical lymph node (LN) metastases based on objective measures and subjective findings...
PURPOSE
We evaluated the current performance of diagnostic ultrasound (US) for detecting cervical lymph node (LN) metastases based on objective measures and subjective findings in comparison to the gold standard, histopathological evaluation.
PATIENTS AND METHODS
From 2007 to 2016, we prospectively included patients with head and neck cancer who were scheduled for surgical therapy including neck dissection. LNs were examined by multimodal US by a level III head and neck sonologist and individually assigned to a map containing six AAO-HNS neck LN levels preoperatively. During the operation, LNs were dissected and then assessed by routine histopathology, with 86% of them examined individually and the remaining LNs (14%) per AAO-HNS neck LN level. The optimal cutoff points (OCPs) of four defined LN diameters and 2D and 3D roundness indices per AAO-HNS neck LN level were determined.
RESULTS
In total, 235 patients were included, and 4539 LNs were analyzed by US, 7237 by histopathology and 2684 by both methods. Of these, 259 (9.65%) were classified as suspicious for metastasis by US, whereas 299 (11.14%) were found to be positive by histopathology. Subjective US sensitivity and specificity were 0.79 and 0.99, respectively. The OCPs of the individual LN diameters and the 2D and 3D roundness index were determined individually for all AAO-HNS neck LN levels. Across all levels, the OCP for the 2D index was 1.79 and the 3D index was 14.97. The predictive performance of all distances, indices, and subjective findings improved with increasing metastasis size. Anticipation of pN stage was best achieved with subjective US findings and the smallest diameter (Cohen's κ = 0.713 and 0.438, respectively).
CONCLUSION
Our LN mapping and meticulous 1:1 node-by-node comparison reveals the usefulness of US for detecting metastatic involvement of neck LNs in head and neck carcinomas as compared to histopathology. The predictive ability for small tumor deposits less than 8 mm in size remains weak and urgently needs improvement.
Topics: Humans; Lymphatic Metastasis; Lymph Nodes; Head and Neck Neoplasms; Neck Dissection; Ultrasonography
PubMed: 37823935
DOI: 10.1007/s00432-023-05439-x -
JCO Clinical Cancer Informatics Jun 2024Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current...
PURPOSE
Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)-driven histopathology image analysis would aid us in better risk stratification of PCa.
MATERIALS AND METHODS
We propose a deep learning, histopathology image-based risk stratification model that combines clinicopathologic data along with hematoxylin and eosin- and Ki-67-stained histopathology images. We train and test our model, using a five-fold cross-validation strategy, on a data set from 502 treatment-naïve PCa patients who underwent radical prostatectomy (RP) between 2000 and 2012.
RESULTS
We used the concordance index as a measure to evaluate the performance of various risk stratification models. Our risk stratification model on the basis of convolutional neural networks demonstrated superior performance compared with Gleason grading and the Cancer of the Prostate Risk Assessment Post-Surgical risk stratification models. Using our model, 3.9% of the low-risk patients were correctly reclassified to be high-risk and 21.3% of the high-risk patients were correctly reclassified as low-risk.
CONCLUSION
These findings highlight the importance of ML as an objective tool for histopathology image assessment and patient risk stratification. With further validation on large cohorts, the digital pathology risk classification we propose may be helpful in guiding administration of adjuvant therapy including radiotherapy after RP.
Topics: Humans; Prostatic Neoplasms; Male; Deep Learning; Neoplasm Grading; Risk Assessment; Prostatectomy; Aged; Middle Aged; Image Processing, Computer-Assisted
PubMed: 38900978
DOI: 10.1200/CCI.23.00184