Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma

et al. 2022

Front. Cell Dev. Biol.

Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

Section: Introductionmentioning

confidence: 99%

RETRACTED: BCNet: A Novel Network for Blood Cell Classification

Zhu

et al. 2022

Front. Cell Dev. Biol.

“…In contrast, the less optimal performance of image classifiers for genomic features such as PAM50 status or TP53 mutation status emphasizes that the accuracy of such models is highly dependent on the strength of the morphological signals relative to a particular goal. It is impossible for a model based on optical microscopic images to directly observe an indel or frameshift mutation in a particular gene, but it is possible to observe morphological impacts resulting from or related to a given mutation 11 , 27 . Hence, these models are more likely to find utility in translational systems where the ultimate goal is improving patient diagnosis, prognosis, and treatment stratification, exemplified in this case, by TP53 mutation status, as assessed by an image model, and representing a proxy for the mutation and its causes and consequences, rather than the mutation alone.…”

Section: Discussionmentioning

confidence: 99%

Integrative multiomics-histopathology analysis for breast cancer classification

et al. 2021

Self Cite

Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.

“…More recently, survival analysis has been approached with a classification task by predicting several periods of survival time divided by specific time points 20 . These classifiers, however, cannot model the risk values with certain survival times and lack independent follow-up cohort.…”

Section: Discussionmentioning

confidence: 99%

“…Chen et al developed a strategy for integrating histology image and genomic features to predict the outcomes of ccRCC patients from TCGA 19 . Marostica et al diagnosed RCC histological subtypes and predicted stage I ccRCC patients' survival outcomes 20 .…”

Section: Introductionmentioning

confidence: 99%

Classifying subtypes and predicting survival of renal cell carcinoma using histopathology image-based deep learning

Geng

et al. 2021

Preprint

Classifying histopathological subtypes and predicting survival of renal cell carcinoma (RCC) patients are critical steps towards treatment. In this work, we first proposed a deep learning method involving patch-based segmentation, intelligent feature extraction and heatmap visualization for classifying RCC into clear cell RCC, papillary RCC, chromophobe RCC, and adjacent benign tissue. This algorithm was trained and validated using 2,374,446 patches, 6,340 whole-slide images, 2,399 patients from The Cancer Genome Atlas and 6 independent centers. The classifiers provided areas under the curves of 0.979 to 0.996 in the internal phase, and 0.914 to 0.995 in the 6-center external phase. Furthermore, a modified deep learning approach comprising automated detection of regions of interest, patch-level learning, and morphological features-based risk scoring was developed for predicting survival of clear cell RCC patients. The prognostication model provided a hazard ratio for poor versus good prognosis of 2.63 [95% confidence interval (CI) 1.53–4.50, P = 4.35e-4] in the testing set, and 2.57 [95% CI 1.43–4.64, P = 1.68e-3] in an independent validation set using multivariable analyses. In conclusion, the developed histopathology image-based deep learning frameworks have the clinical potential to assist pathologists in systematically evaluating histological information of RCC patients.