Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

Cheng, Jun; Han, Zhi; Mehra, Rohit; Shao, Wei; Cheng, Monica; Feng, Qianjin; Ni, Dong; Huang, Kun; Liu, Cheng; Zhang, Jie

doi:10.1038/s41467-020-15671-5

Cited by 60 publications

(41 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have shown that image features and machine learning techniques can discern subtle differences that are not readily noticeable to pathologists between tissues from patients with different disease subtypes, cancer grades, and survival; 28 , 29 , 30 , 31 here, we further extend the scope to predict response to NAC in MIBC. Spatial heterogeneity is a hallmark of cancer, and features of the tumor microenvironment (TME) may drive tumor responses to specific therapies.…”

Section: Discussionmentioning

confidence: 98%

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

Bivalacqua

Kates

et al. 2021

Cell Reports Medicine

View full text Add to dashboard Cite

Summary Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%–73% accuracy). Interestingly, one model—using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features—is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC.

show abstract

Section: Discussionmentioning

confidence: 98%

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

Bivalacqua

Kates

et al. 2021

Cell Reports Medicine

View full text Add to dashboard Cite

show abstract

“…In this study, we used a 5-fold cross-validation to assess the prediction performance of the model because it was the most commonly used method for machine learning-based medical problem exploration (33)(34)(35)(36)(37). Specifically, the available training set was divided into five roughly equal-sized subsets: the training set and the validation (or internal validation) set.…”

Section: Validationmentioning

confidence: 99%

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Wang

Sun

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

Background: Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. We aimed to develop an artificial intelligence algorithm that can predict sepsis early.Methods: This was a secondary analysis of an observational cohort study from the Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University. A total of 4,449 infected patients were randomly assigned to the development and validation data set at a ratio of 4:1. After extracting electronic medical record data, a set of 55 features (variables) was calculated and passed to the random forest algorithm to predict the onset of sepsis.Results: The pre-procedure clinical variables were used to build a prediction model from the training data set using the random forest machine learning method; a 5-fold cross-validation was used to evaluate the prediction accuracy of the model. Finally, we tested the model using the validation data set. The area obtained by the model under the receiver operating characteristic (ROC) curve (AUC) was 0.91, the sensitivity was 87%, and the specificity was 89%.Conclusions: This newly established machine learning-based model has shown good predictive ability in Chinese sepsis patients. External validation studies are necessary to confirm the universality of our method in the population and treatment practice.

show abstract

“…In view of the color variations between different institutions, before feature extraction we normalized the color appearance of the images using a structure-preserving color normalization algorithm (37). We manually labeled tumor and non-tumor regions in whole-slide images and extracted a total of 150 patient-level image features within the tumor regions using a histopathological image analysis pipeline we previously developed in our lab (38). The feature extraction pipeline is comprised of three steps: segmenting cell nuclei, extracting celllevel features, and summarizing cell-level features into patientlevel features.…”

Section: Extraction Of Quantitative Features From Whole-slide Imagesmentioning

confidence: 99%

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

et al. 2021

Self Cite

View full text Add to dashboard Cite

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

show abstract

Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

Cited by 60 publications

References 45 publications

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Contact Info

Product

Resources

About