BRCA1- A ssociated protein 1 (BAP1) immunohistochemical expression as a diagnostic tool in malignant pleural mesothelioma classification: A large retrospective study

Malignant pleural mesothelioma (MPM) is a highly aggressive disease with limited therapeutic options. Histological subtype remains among the most reliable prognostic factors, because the epithelioid subtype associated with the best prognosis and the sarcomatoid subtype with the worst. The biphasic subtype has an intermediate prognosis, but its definitive histological diagnosis may be challenging owing to the difficulty of assessing the neoplastic nature of the stromal component. Recent data identified BRCA1-associated protein 1 gene (BAP1) as one of the most frequently mutated genes in MPM. Immunohistochemical testing for BRCA1-associated protein 1 (BAP1) has been proposed to be predictive for the detection of BAP1 mutation in neoplastic cells. The aim of the present study was to define the diagnostic usefulness of immunohistochemical determination of BAP1 in MPM, with clinicopathological correlation

Detection and characterization of classical and "uncommon" exon 19 Epidermal Growth Factor Receptor mutations in lung cancer by pyrosequencing

BACKGROUND: The management of advanced stage non-small cell lung cancer is increasingly based on diagnostic and predictive analyses performed mostly on limited amounts of tumor tissue. The evaluation of Epidermal Growth Factor Receptor (EGFR) mutations have emerged as the strongest predictor of response to EGFR-tyrosine kinase inhibitors mainly in patients with adenocarcinoma. Several EGFR mutation detection techniques are available, having both sensitivity and specificity issues, being the Sanger sequencing technique the reference standard, with the limitation of a relatively high amount of mutated cells needed for the analysis. METHODS: A novel nucleotide dispensation order for pyrosequencing was established allowing the identification and characterization of EGFR mutation not definable with commercially and clinically approved kits, and validated in a consecutive series of 321 lung cancer patients (246 biopsies or cytology samples and 75 surgical specimens). RESULTS: 61/321 (19%) mutated cases were detected, 17 (27.9%) in exon 21 and 44 (72.1%) in exon 19, these latter corresponding to 32/44 (72.7%) classical and 12/44 (27.3%) uncommon mutations. Furthermore, a novel, never reported, point mutation, was found, which determined a premature stop codon in the aminoacidic sequence that resulted in a truncated protein in the tyrosine kinase domain, thus impairing the inhibitory effect of specific therapy. CONCLUSIONS: The novel dispensation order allows to detect and characterize both classical and uncommon EGFR mutations. Although several phase III studies in genotypically defined groups of patients are already available, further prospective studies assessing the role of uncommon EGFR mutations are warranted

Implementation of preventive and predictive BRCA testing in patients with breast, ovarian, pancreatic, and prostate cancer: a position paper of Italian Scientific Societies

Constitutional BRCA1/BRCA2 pathogenic or likely pathogenic variants (PVs) are associated with an increased risk for developing breast and ovarian cancers. Current evidence indicates that BRCA1/2 PVs are also associated with pancreatic cancer, and that BRCA2 PVs are associated with prostate cancer risk. The identification of carriers of constitutional PVs in the BRCA1/2 genes allows the implementation of individual and family prevention pathways, through validated screening programs and risk-reducing strategies. According to the relevant and increasing therapeutic predictive implications, the inclusion of BRCA testing in the routine management of patients with breast, ovarian, pancreatic and prostate cancers represent a key requirement to optimize medical or surgical therapeutic and prevention decision-making, and access to specific anticancer therapies. Therefore, accurate patient selection, the use of standardized and harmonized procedures, and adherence to homogeneous testing criteria, are essential elements to implement BRCA testing in clinical practice. This consensus position paper has been developed and approved by a multidisciplinary Expert Panel of 64 professionals on behalf of the AIOM–AIRO–AISP–ANISC–AURO–Fondazione AIOM–SIAPEC/IAP–SIBioC–SICO–SIF–SIGE–SIGU–SIU–SIURO–UROP Italian Scientific Societies, and a patient association (aBRCAdaBRA Onlus). The working group included medical, surgical and radiation oncologists, medical and molecular geneticists, clinical molecular biologists, surgical and molecular pathologists, organ specialists such as gynecologists, gastroenterologists and urologists, and pharmacologists. The manuscript is based on the expert consensus and reports the best available evidence, according to the current eligibility criteria for BRCA testing and counseling, it also harmonizes with current Italian National Guidelines and Clinical Recommendations

Efficacy of Visual Feedback Training for Motor Recovery in Post-Operative Subjects with Knee Replacement: A Randomized Controlled Trial

To evaluate the effects of visual feedback training on motor recovery in postoperative patients with a total knee replacement (TKR). The performance of 40 first-ever TKR patients (27 females; mean age: 70.5 (67.2–74.0) years) was evaluated in a single center, single-blind, randomized controlled study. The patients were randomly and equally distributed into two demographically/clinically matched groups undergoing experimental or traditional treatments. All patients have been treated in a 1 h session, 2/day for 5 days a week, for six consecutive weeks. The first group (“control”) underwent conventional physical therapy, whereas the experimental group received advanced knee training with visual feedback using the TecnoBody® device (Walker View 3.0 SCX, Dalmine (BG), Italy). The clinical scales and kinematic parameters coming from the gait analysis were evaluated to demonstrate the dynamic balance function in a standing position before and after each treatment. After the treatment, both experimental and control groups improved significantly and similarly, as measured by the clinical scales (Numeric Rating Scale for Pain and Barthel index). A significant boosting of the motor performance was detected in the experimental group with respect to the control group in the terms of symmetry index 84 (80.8–85.4) vs. 87.15 (84–92.8) p = 0.001 *; single stance support 34.9 (34.1–36.5) vs. 37.8 (36.6–38.9); p < 0.001; and obliquity parameters 58.65 (51.3–70.3) vs. 73 (62.3–82.1); p < 0.001. Applying visual feedback training in addition to traditional rehabilitation strategies improves the knee function and motor control in postoperative TKR patients

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations