2 research outputs found
Comparison of HER-2 overexpression in primary breast cancer and metastatic sites and its effect on biological targeting therapy of metastatic disease
HER-2 overexpression, a predictive marker of tumour aggressiveness and responsiveness to therapy, occurs in 20–30% of breast cancer. Although breast cancer is a heterogeneous disease, HER-2 measurement is carried out in primary tumour. This study aims to evaluate HER-2 overexpression in primary and metastases and its effect on treatment decisions. Biopsies from primary breast cancer and corresponding metastases from 58 patients were studied. HER-2 overexpression was evaluated immunohistochemically in all primary and metastatic sites. Positive overexpression in primary and/or metastases was confirmed by fluorescence in situ hybridisation (FISH). Discordance in HER-2 overexpression between primary and metastatic sites was 14% (eight of 58 patients). Concordance was found in 50 (86%) of patients (95% CI: 77–95). In one patient (2%), HER-2 was negative in metastasis but positive in primary. In seven (12%) patients, HER-2 was positive in metastases and negative in primary (95% CI: 3.7–20), and three of them responded to trastuzumab. Gene amplification by FISH was found in all cases with HER-2 positive (+2 and +3) by immunohistochemistry. Our data suggest that a possible discordance of HER-2 overexpression between primary and metastases should be considered when making treatment decisions in patients with primary HER-2-negative tumours
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Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide