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Frontiers in Oncology 2024Gold standard for the establishment of the diagnosis of myelodysplastic syndromes (MDS) are cytomorphological features of hematopoietic cells in peripheral blood and...
Gold standard for the establishment of the diagnosis of myelodysplastic syndromes (MDS) are cytomorphological features of hematopoietic cells in peripheral blood and bone marrow aspirates. There is increasing evidence that bone marrow histomorphology not only aids in the diagnosis of MDS but can provide additional prognostic information, particularly through assessment of fibrosis and cellularity. However, there is only sparse data on direct comparison between histological and cytomorphological findings within the same MDS patient cohort. Therefore, we performed such an analysis under exceptionally well-standardized conditions. We reexamined biopsy material of 128 patients from the Düsseldorf MDS registry who underwent bone marrow trephine biopsy (in addition to bone marrow aspiration) at the time of diagnosis, addressing the following items: a. Analysis of concordance of diagnoses made by histology and cytomorphology b. Analysis of additional information by histology with regard to the diagnosis and prognosis. The respective biomaterials were available at our institution and had been processed according to unchanged protocols between 1992 and 2010. Fresh histopathological sections were obtained from the tissue blocks, stained under identical conditions and re-assessed by a designated expert pathologist (C.B.) without knowledge of the previous histopathological report or the respective cytomorphological diagnosis. The latter, likewise, was uniformly made by the same expert cytomorphologist (U.G.). Histopathology of bone marrow trephine biopsies reliably captured the diagnosis of MDS. Assignment to the diagnostic WHO subgroup was not entirely concordant with cytomorphology, mainly due to incongruences between the proportion of CD34-positive cells on histopathology and the cytomorphological blast count. Histopathology provided additional diagnostic and prognostic information with high diagnostic and prognostic significance, such as fibrosis. Likewise, histopathology allowed more reliable estimation of bone marrow cellularity.
PubMed: 38665949
DOI: 10.3389/fonc.2024.1359115 -
Journal of Pathology Informatics Dec 2024Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important... (Review)
Review
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of their generalizability. The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. A systematic search of MEDLINE, EMBASE, CINAHL, IEEE, MICCAI, and SPIE conferences was performed for studies published between January 2010 and February 2022. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed, and the results were narratively described. Of the 2011 non-duplicated citations, 8 journal articles and 2 conference proceedings met inclusion criteria. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. The hazard ratios in the EV of prognostic ML models were between 1.7 (95% CI, 1.2-2.6) and 1.8 (95% CI, 1.3-2.7) to predict distant disease-free survival; 1.91 (95% CI, 1.11-3.29) for recurrence, and between 0.09 (95% CI, 0.01-0.70) and 0.65 (95% CI, 0.43-0.98) for overall survival, using clinical data as ground truth. Despite EV being an important step before the clinical application of a ML model, it hasn't been performed routinely. The large variability in the training/validation datasets, methods, performance metrics, and reported information limited the comparison of the models and the analysis of their results. Increasing the availability of validation datasets and implementing standardized methods and reporting protocols may facilitate future analyses.
PubMed: 38089005
DOI: 10.1016/j.jpi.2023.100348 -
Nature Medicine May 2024Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions....
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
Topics: Humans; Deep Learning; Neoplasms, Unknown Primary; Female; Male; Aged; Middle Aged; ROC Curve; Adult; Cytodiagnosis; Aged, 80 and over; Ascites; Cytology
PubMed: 38627559
DOI: 10.1038/s41591-024-02915-w -
International Journal of Computer... Jan 2024Sentinel lymph node biopsy for oral and oropharyngeal squamous cell carcinoma is a well-established staging method. One variation is to inject a radioactive tracer near...
INTRODUCTION
Sentinel lymph node biopsy for oral and oropharyngeal squamous cell carcinoma is a well-established staging method. One variation is to inject a radioactive tracer near the primary tumor of the patient. After a few minutes, audio feedback from an external hand-held [Formula: see text]-detection probe can monitor the uptake into the lymphatic system. Such probes place a high cognitive load on the surgeon during the biopsy, as they require the simultaneous use of both hands and the skills necessary to correlate the audio signal with the location of tracer accumulation in the lymph nodes. Therefore, an augmented reality (AR) approach to directly visualize and thus discriminate nearby lymph nodes would greatly reduce the surgeons' cognitive load.
MATERIALS AND METHODS
We present a proof of concept of an AR approach for sentinel lymph node biopsy by ex vivo experiments. The 3D position of the radioactive [Formula: see text]-sources is reconstructed from a single [Formula: see text]-image, acquired by a stationary table-attached multi-pinhole [Formula: see text]-detector. The position of the sources is then visualized using Microsoft's HoloLens. We further investigate the performance of our SLNF algorithm for a single source, two sources, and two sources with a hot background.
RESULTS
In our ex vivo experiments, a single [Formula: see text]-source and its AR representation show good correlation with known locations, with a maximum error of 4.47 mm. The SLNF algorithm performs well when only one source is reconstructed, with a maximum error of 7.77 mm. For the more challenging case to reconstruct two sources, the errors vary between 2.23 mm and 75.92 mm.
CONCLUSION
This proof of concept shows promising results in reconstructing and displaying one [Formula: see text]-source. Two simultaneously recorded sources are more challenging and require further algorithmic optimization.
Topics: Humans; Sentinel Lymph Node Biopsy; Augmented Reality; Lymph Nodes; Neoplasm Staging
PubMed: 37747574
DOI: 10.1007/s11548-023-03014-w -
Open Veterinary Journal Oct 2023Only 27 cases of equine conjunctival haemangiosarcoma have been reported in the literature over the past 37 years. Out of these, 22% of cases were lost to follow-up, 52%...
BACKGROUND
Only 27 cases of equine conjunctival haemangiosarcoma have been reported in the literature over the past 37 years. Out of these, 22% of cases were lost to follow-up, 52% were euthanized, and 26% survived. A scarcity of cases and information is available for this rarely seen conjunctival tumour.
AIM
To describe the clinical features, management, and outcome of conjunctival hemangiosarcoma in seven horses in the UK.
METHODS
Optivet medical records were reviewed for equine cases seen or advised on with a histopathological diagnosis of conjunctival haemangiosarcoma between January 2013 and March 2023. Medical records were accessed for details of signalment, history, management, and follow-up. Histopathology was used to confirm the diagnosis of haemangiosarcoma and assess the surgical margins. Immunohistochemistry was performed in a minority of cases with poorly differentiated solid tumours to support vascular lineage.
RESULTS
Seven eyes from seven horses (five geldings and two mares) with a mean age of 16 years and median of 18 years (range 10-21 years) met the criteria. Serosanguinous discharge was seen in six eyes. All eyes were managed surgically; 4 by exenteration and 3 by conjunctivectomy/keratectomy. Adjunctive cryotherapy was performed in two eyes. Metastatic disease in the ipsilateral parotid salivary gland, confirmed with histopathology, was seen in one horse. Surgical margins were clear in all but one eye. Solar elastosis was noted in five eyes. All horses were healthy at the last follow-up (0.2-5 years, mean 2.9 years, and median 2 years).
CONCLUSION
Equine conjunctival haemangiosarcoma is rare. Serosanguinous ocular discharge is a common clinical sign. Early surgical excision is highly effective. Solar elastosis is a common histopathological feature, suggesting a role for UV-light in the pathogenesis.
Topics: Horses; Animals; Male; Female; Hemangiosarcoma; Margins of Excision; United Kingdom; Horse Diseases
PubMed: 38027397
DOI: 10.5455/OVJ.2023.v13.i10.17 -
Skin Appendage Disorders Aug 2023Localized longitudinal erythronychia is defined as a single nail with a longitudinal red band extending the length of a nail plate. It has a broad differential of benign...
INTRODUCTION
Localized longitudinal erythronychia is defined as a single nail with a longitudinal red band extending the length of a nail plate. It has a broad differential of benign and malignant etiologies, and is rarely due to benign vascular proliferations.
CASE PRESENTATION
We present a unique case of nail unit arteriovenous hemangioma presenting as longitudinal erythronychia of the left thumbnail in a 76-year-old male. The band was 6 mm and encompassed over 40% of the surface area of the nail plate. Dermoscopy showed red bands that were regular in terms of color, but not thickness or spacing. Due to concern for an amelanotic melanoma, a longitudinal excision was performed. Histopathology was consistent with a diagnosis of nail unit arteriovenous hemangioma.
CONCLUSION
Arteriovenous hemangiomas were rarely present in the nail unit. They can be present as a blue or red nodule/macule, or as longitudinal erythronychia. Diagnosis often requires an excisional biopsy, with histopathology notable for a proliferation of multiple thick- and thin-walled vascular structures lined by a flattened endothelium. Our case emphasizes the need to consider vascular proliferations, such as arteriovenous hemangioma, in the differential diagnosis of longitudinal erythronychia.
PubMed: 37588479
DOI: 10.1159/000530739 -
La Radiologia Medica Aug 2023To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the...
PURPOSE
To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.
MATERIAL AND METHODS
This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort.
RESULTS
Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474).
CONCLUSION
MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.
Topics: Humans; Retrospective Studies; Magnetic Resonance Imaging; Liposarcoma; Lipoma; Extremities; Machine Learning
PubMed: 37335422
DOI: 10.1007/s11547-023-01657-y -
BMC Medical Genomics Feb 2024Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been... (Review)
Review
BACKGROUND
Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biological knowledge from pathology slides and genomic data in cancer. In addition, a number of recent studies have introduced multimodal DL models designed to simultaneously process both images from pathology slides and genomic data as inputs. By comparing patterns from one data modality with those in another, multimodal DL models are capable of achieving higher performance compared to their unimodal counterparts. However, the application of these methodologies across various tumor entities and clinical scenarios lacks consistency.
METHODS
Here, we present a systematic survey of the academic literature from 2010 to November 2023, aiming to quantify the application of DL for pathology, genomics, and the combined use of both data types. After filtering 3048 publications, our search identified 534 relevant articles which then were evaluated by basic (diagnosis, grading, subtyping) and advanced (mutation, drug response and survival prediction) application types, publication year and addressed cancer tissue.
RESULTS
Our analysis reveals a predominant application of DL in pathology compared to genomics. However, there is a notable surge in DL incorporation within both domains. Furthermore, while DL applied to pathology primarily targets the identification of histology-specific patterns in individual tissues, DL in genomics is more commonly used in a pan-cancer context. Multimodal DL, on the contrary, remains a niche topic, evidenced by a limited number of publications, primarily focusing on prognosis predictions.
CONCLUSION
In summary, our quantitative analysis indicates that DL not only has a well-established role in histopathology but is also being successfully integrated into both genomic and multimodal applications. In addition, there is considerable potential in multimodal DL for harnessing further advanced tasks, such as predicting drug response. Nevertheless, this review also underlines the need for further research to bridge the existing gaps in these fields.
Topics: Humans; Neoplasms; Deep Learning; Precision Medicine; Genomics; Mutation
PubMed: 38317154
DOI: 10.1186/s12920-024-01796-9 -
Bioengineering (Basel, Switzerland) Nov 2023Breast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing... (Review)
Review
Breast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing breast cancer sometime in their life is about 13%, and this incident rate has increased by 0.5% per year in recent years. A biopsy is done when screening tests and imaging results show suspicious breast changes. Advancements in computer-aided system capabilities and performance have fueled research using histopathology images in cancer diagnosis. Advances in machine learning and deep neural networks have tremendously increased the number of studies developing computerized detection and classification models. The dataset-dependent nature and trial-and-error approach of the deep networks' performance produced varying results in the literature. This work comprehensively reviews the studies published between 2010 and 2022 regarding commonly used public-domain datasets and methodologies used in preprocessing, segmentation, feature engineering, machine-learning approaches, classifiers, and performance metrics.
PubMed: 38002413
DOI: 10.3390/bioengineering10111289 -
Brain Pathology (Zurich, Switzerland) Jul 2023Neuroinflammation has been implicated in frontotemporal lobar degeneration (FTLD) pathophysiology, including in genetic forms with microtubule-associated protein tau...
Neuroinflammation has been implicated in frontotemporal lobar degeneration (FTLD) pathophysiology, including in genetic forms with microtubule-associated protein tau (MAPT) mutations (FTLD-MAPT) or chromosome 9 open reading frame 72 (C9orf72) repeat expansions (FTLD-C9orf72). Iron accumulation as a marker of neuroinflammation has, however, been understudied in genetic FTLD to date. To investigate the occurrence of cortical iron accumulation in FTLD-MAPT and FTLD-C9orf72, iron histopathology was performed on the frontal and temporal cortex of 22 cases (11 FTLD-MAPT and 11 FTLD-C9orf72). We studied patterns of cortical iron accumulation and its colocalization with the corresponding underlying pathologies (tau and TDP-43), brain cells (microglia and astrocytes), and myelination. Further, with ultrahigh field ex vivo MRI on a subset (four FTLD-MAPT and two FTLD-C9orf72), we examined the sensitivity of T2*-weighted MRI for iron in FTLD. Histopathology showed that cortical iron accumulation occurs in both FTLD-MAPT and FTLD-C9orf72 in frontal and temporal cortices, characterized by a diffuse mid-cortical iron-rich band, and by a superficial cortical iron band in some cases. Cortical iron accumulation was associated with the severity of proteinopathy (tau or TDP-43) and neuronal degeneration, in part with clinical severity, and with the presence of activated microglia, reactive astrocytes and myelin loss. Ultra-high field T2*-weighted MRI showed a good correspondence between hypointense changes on MRI and cortical iron observed on histology. We conclude that iron accumulation is a feature of both FTLD-MAPT and FTLD-C9orf72 and is associated with pathological severity. Therefore, in vivo iron imaging using T2*-weighted MRI or quantitative susceptibility mapping may potentially be used as a noninvasive imaging marker to localize pathology in FTLD.
Topics: Humans; C9orf72 Protein; Neuroinflammatory Diseases; Progranulins; Frontotemporal Lobar Degeneration; tau Proteins; Frontotemporal Dementia; DNA-Binding Proteins
PubMed: 36974379
DOI: 10.1111/bpa.13158