Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD:  A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors).

Efficacy of zoledronic acid for the elimination of disseminated tumor cells in a clinically relevant, spontaneously metastatic prostate cancer xenograft model.

Bone metastases develop in >90 % of patients with castration-resistant prostate cancer (PCa) through complex interactions between the bone microenvironment and tumor cells. Previous androgen-deprivation therapy (ADT), which is known to cause bone loss, as well as anti-resorptive agents such as zoledronic acid (ZA), used to prevent skeletal complications, may influence these interactions and thereby the growth of disseminated tumor cells (DTC) in the bone marrow (BM). Here, a spontaneously metastatic xenograft tumor model of human PCa was further optimized to mimic the common clinical situation of ADT (castration) combined with primary tumor resection in vivo.

Quantitative analysis of submucosal excision depth in endoscopic resection for early Barrett's cancer.

BACKGROUND : Following endoscopic resection of early-stage Barrett's esophageal adenocarcinoma (BEA), further oncologic management then fundamentally relies upon the accurate assessment of histopathologic risk criteria, which requires there to be sufficient amounts of submucosal tissue in the resection specimens. METHODS : In 1685 digitized tissue sections from endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) performed for 76 early BEA cases from three experienced centers, the submucosal thickness was determined, using software developed in-house. Neoplastic lesions were manually annotated.

Multi-scale fully convolutional neural networks for histopathology image segmentation: From nuclear aberrations to the global tissue architecture.

Histopathologic diagnosis relies on simultaneous integration of information from a broad range of scales, ranging from nuclear aberrations (≈O(0.1μm)) through cellular structures (≈O(10μm)) to the global tissue architecture (⪆O(1mm)). To explicitly mimic how human pathologists combine multi-scale information, we introduce a family of multi-encoder fully-convolutional neural networks with deep fusion.

Possible tumour cell reimplantation during curative endoscopic therapy of superficial Barrett's carcinoma.

Background And Aims: Endoscopic resection has been established as curative therapy for superficial cancer arising from Barrett's oesophagus (BE); recurrences are very rare. Based on a case series with unusual and massive early recurrences, we analyse the issue of tumour cell reimplantation.

Methods: This hypothesis was developed on the basis of two out of seven patients treated by circumferential (n=6) or nearly circumferential (n=1) en bloc and R0 endoscopic resection of T1 neoplastic BE.

Digital Health meets Hamburg integrated medical degree program iMED: concept and introduction of the new interdisciplinary 2 track Digital Health.

Digitalization in medicine is transforming the everyday work and the environment of current and future physicians - and thereby brings new competencies required by the medical profession. The necessity for a curricular integration of related digital medicine and, in more general, digital health topics is mostly undisputed; however, few specific concepts and experience reports are available. Therefore, the present article reports on the aims, the implementation, and the initial experiences of the integration of the topic Digital Health as a longitudinal elective course (2 track) into the integrated medical degree program iMED in Hamburg.

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets.

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up a variety of new perspectives for endoscopy, such as automated lesion detection and the precise prediction of a lesion's histology by its endoscopic appearance. Whilst early experiments do suggest an enormous potential for these methods, public expectations on their application in various fields of medicine sometimes appear to be grounded on general fascination rather than detailed understanding of their inner workings. Based on a selective review of the literature, this article shall convey an intuitive understanding of the underlying methods in order to help close the gap between functioning and fascination and allow for a realistic discussion of their perspectives and limitations in endoscopy.

Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.

Objective: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imbalance encountered in real-world multi-class datasets.

Modeling Growth of Tumors and Their Spreading Behavior Using Mathematical Functions.

Computer simulations of the spread of malignant tumor cells in an entire organism provide important insights into the mechanisms of metastatic progression. Key elements for the usefulness of these models are the adequate selection of appropriate mathematical models describing the tumor growth and its parametrization as well as a proper choice of the fractal dimension of the blood vessels in the primary tumor. In addition, survival in the bloodstream and evasion into the connective spaces of the target organ of the future metastasis have to be modeled.

Locally Ablative Radiation Therapy of a Primary Human Small Cell Lung Cancer Tumor Decreases the Number of Spontaneous Metastases in Two Xenograft Models.

Purpose: To investigated the influence of radiation therapy (RT), surgery (OP), radio-chemotherapy (RChT), or chemotherapy (ChT) on small cell lung cancer metastases in 2 xenograft models.

Methods And Materials: A total of 1 × 10 human small cell lung cancer cells (OH1, H69) were subcutaneously injected into severe combined immunodeficiency mice to form a local primary tumor node at the lower trunk. Radiation therapy, OP, RChT, or ChT were started after development of palpable tumors.

Radiotherapy and chemotherapy change vessel tree geometry and metastatic spread in a small cell lung cancer xenograft mouse tumor model.

Background: Tumor vasculature is critical for tumor growth, formation of distant metastases and efficiency of radio- and chemotherapy treatments. However, how the vasculature itself is affected during cancer treatment regarding to the metastatic behavior has not been thoroughly investigated. Therefore, the aim of this study was to analyze the influence of hypofractionated radiotherapy and cisplatin chemotherapy on vessel tree geometry and metastasis formation in a small cell lung cancer xenograft mouse tumor model to investigate the spread of malignant cells during different treatments modalities.