Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer

Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides

Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localised scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Methods and Results: Regions of tumour in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883 respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. Conclusions: This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration

Outcome of ATP-based tumor chemosensitivity assay directed chemotherapy in heavily pre-treated recurrent ovarian carcinoma

BACKGROUND: We wished to evaluate the clinical response following ATP-Tumor Chemosensitivity Assay (ATP-TCA) directed salvage chemotherapy in a series of UK patients with advanced ovarian cancer. The results are compared with that of a similar assay used in a different country in terms of evaluability and clinical endpoints. METHODS: From November 1998 to November 2001, 46 patients with pre-treated, advanced ovarian cancer were given a total of 56 courses of chemotherapy based on in-vitro ATP-TCA responses obtained from fresh tumor samples or ascites. Forty-four patients were evaluable for results. Of these, 18 patients had clinically platinum resistant disease (relapse < 6 months after first course of chemotherapy). There was evidence of cisplatin resistance in 31 patients from their first ATP-TCA. Response to treatment was assessed by radiology, clinical assessment and tumor marker level (CA 125). RESULTS: The overall response rate was 59% (33/56) per course of chemotherapy, including 12 complete responses, 21 partial responses, 6 with stable disease, and 15 with progressive disease. Two patients were not evaluable for response having received just one cycle of chemotherapy: if these were excluded the response rate is 61%. Fifteen patients are still alive. Median progression free survival (PFS) was 6.6 months per course of chemotherapy; median overall survival (OAS) for each patient following the start of TCA-directed therapy was 10.4 months (95% confidence interval 7.9-12.8 months). CONCLUSION: The results show similar response rates to previous studies using ATP-TCA directed therapy in recurrent ovarian cancer. The assay shows high evaluability and this study adds weight to the reproducibility of results from different centre

Guidance for laboratories performing molecular pathology for cancer patients

Molecular testing is becoming an important part of the diagnosis of any patient with cancer. The challenge to laboratories is to meet this need, using reliable methods and processes to ensure that patients receive a timely and accurate report on which their treatment will be based. The aim of this paper is to provide minimum requirements for the management of molecular pathology laboratories. This general guidance should be augmented by the specific guidance available for different tumour types and tests. Preanalytical considerations are important, and careful consideration of the way in which specimens are obtained and reach the laboratory is necessary. Sample receipt and handling follow standard operating procedures, but some alterations may be necessary if molecular testing is to be performed, for instance to control tissue fixation. DNA and RNA extraction can be standardised and should be checked for quality and quantity of output on a regular basis. The choice of analytical method(s) depends on clinical requirements, desired turnaround time, and expertise available. Internal quality control, regular internal audit of the whole testing process, laboratory accreditation, and continual participation in external quality assessment schemes are prerequisites for delivery of a reliable service. A molecular pathology report should accurately convey the information the clinician needs to treat the patient with sufficient information to allow for correct interpretation of the result. Molecular pathology is developing rapidly, and further detailed evidence-based recommendations are required for many of the topics covered here

An Examination of the Validity of an iOS-Based Heart Rate and Pulse Oximetry App During And after Moderate Intensity Exercise

Fitness tracking apps are popular. There is little data validating their use during and after exercise. PURPOSE: Validate an iOS-based pulse oximeter against a fingertip pulse oximeter and a Polar® heart rate monitor during moderate intensity exercise and recovery. METHODS: Age Estimated Maximal Heart Rate (AEMHR) was calculated for fifteen college-aged students. Participants completed a 30-minute running trial divided into three 10-minute segments intended to elicit heart rate responses of 60, 70, and 80% of AEMHR. Heart rate and oxygen saturation data were collected at five and nine minutes of each segment. RESULTS: At 60% the digiDoc® app exhibited a low correlation when compared to the Polar® heart rate monitor. At 70% the digiDoc® app exhibited a low correlation when compared to the fingertip oximeter. During recovery the digiDoc® app exhibited high correlation values at 5PE and 10PE when compared to the Polar® heart rate monitor. The digiDoc® app exhibited a high correlation at 5PE when compared to the fingertip pulse oximeter. CONCLUSION: There is little evidence to suggest the digiDoc® app accurately measures pulse rate and oxygen saturation during exercise or recovery. However, various issues could have led to erroneous readings. This argues for the continuation of this work

Validation of digital pathology imaging for primary histopathological diagnosis

Aims: Digital pathology (DP) offers advantages over glass slide microscopy (GS), but data demonstrating a statistically valid equivalent (i.e. non-inferior) performance of DP against GS are required to permit its use in diagnosis. The aim of this study is to provide evidence of non-inferiority. Methods and results: Seventeen pathologists re-reported 3017 cases by DP. Of these, 1009 were re-reported by the same pathologist, and 2008 by a different pathologist. Re-examination of 10 138 scanned slides (2.22 terabytes) produced 72 variances between GS and DP reports, including 21 clinically significant variances. Ground truth lay with GS in 12 cases and with DP in nine cases. These results are within the 95% confidence interval for existing intraobserver and interobserver variability, proving that DP is non-inferior to GS. In three cases, the digital platform was deemed to be responsible for the variance, including a gastric biopsy, where Helicobacter pylori only became visible on slides scanned at the ×60 setting, and a bronchial biopsy and penile biopsy, where dysplasia was reported on DP but was not present on GS. Conclusions: This is one of the largest studies proving that DP is equivalent to GS for the diagnosis of histopathology specimens. Error rates are similar in both platforms, although some problems e.g. detection of bacteria, are predictable

An introduction to the WHO 5th edition 2022 classification of testicular tumours

The 5th edition of the World Health Organisation Blue Book was published recently and includes a comprehensive update on testicular tumours. This builds upon the work of the 4th edition, retaining its structure and main nomenclature, including the use of the term 'germ cell neoplasia in situ' (GCNIS) for the pre-invasive lesion of most germ cell tumours and division from those not derived from GCNIS. While there have been important developments in understanding the molecular underpinnings of testicular cancer, this updated classification paradigm and approach remains rooted in morphology. Nomenclature changes include replacement of the term 'primitive neuroectodermal tumour' by 'embryonic neuroectodermal tumour' based on the non-specificity of the former term and to separate these tumours clearly from Ewing sarcoma. Seminoma is placed in a germinoma family of tumours emphasising relation to those tumours at other sites. Criteria for the diagnosis of 'teratoma with somatic transformation' have been modified to not include variable field size assessments. The word 'carcinoid' has been changed to 'neuroendocrine tumour', with most examples in the testis now classified as 'prepubertal type testicular neuroendocrine tumour'. For sex cord-stromal tumours, the use of mitotic counts per high-power field has been changed to per mm2 for malignancy assessments, and the new entities, 'signet ring stromal tumour' and 'myoid gonadal stromal tumour', are defined. Well-differentiated papillary mesothelial tumour has now been defined as tumour type with a favourable prognosis. Sertoliform cystadenoma has been removed as an entity from testicular adnexal tumours and placed with Sertoli cell tumours

Simultaneous detection of lung fusions using a multiplex RT-PCR next generation sequencing-based approach:A multi-institutional research study

Contains fulltext : 195300.pdf (publisher's version ) (Open Access

A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks

Breast cancer is one of the most common cancers affecting women worldwide. They include a group of malignant neoplasms with a variety of biological, clinical, and histopathological characteristics. There are more than 35 different histological forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch matching tools allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the WHO breast taxonomy (Classification of Tumours 5th Ed.) spanning 35 tumour types. We visualized all tumour types using deep features extracted from a state-of-the-art deep learning model, pre-trained on millions of diagnostic histopathology images from the TCGA repository. Furthermore, we test the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the WHO breast taxonomy data reached over 88% accuracy when validating through "majority vote" and more than 91% accuracy when validating using top-n tumour types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive