Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters.

Multimodal Deep Learning for Prognosis Prediction in Renal Cancer.

Background: Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data.

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

Background: Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS.

Patients And Methods: Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS.

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

Background: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications.

Objective: In the current study, we tried to predict the molecular subtype of MIBC samples from conventional histomorphology alone using deep learning.

Influence of knowledge on iodine content in foodstuffs and prophylactic usage of iodized salt on urinary iodine excretion and thyroid volume of adults in southern Germany.

Thyroid volume, urinary iodine excretion as well as personal nutritional knowledge and individual iodine prophylaxis were determined during a health education program on iodine deficiency and prophylaxis in 1992. Participants were 472 male and 568 female (mean age 27.7 years) students and employees of five universities in the southern part of Germany.