Deep learning can predict survival directly from histology in clear cell renal cell carcinoma

Abstract

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9–68.1%), 86.2% (95%-CI: 81.8–90.5%), 44.9% (95%-CI: 40.2–49.6%), and 0.70 (95%-CI: 0.69–0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70–8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60–5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92–4.94, p = 0.08) on external validation. The results demonstrate that the CNN’s image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

Deep learning applications for kidney histology analysis

“…Deep learning was used for several applications in analysis of kidney histology, summarized in Table 2 [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62].…”

“…Deep learning was used for several applications in analysis of kidney histology, summarized in Table 2[48–62].…”

Application of Deep Learning in Cancer Prognosis Prediction Model

“…The AUCs of the model on the internal and external validation sets were 0.671 and 0.657, respectively, indicating that the model can effectively predict the overall survival of patients with gastric cancer and provide a basis for clinical treatment plan selection with high robustness. Wessels et al 22 concluded that CNN-based image analysis has the potential to improve risk stratification in clear-cell renal cell carcinoma and investigated CNN-based extraction of relevant image features from H&E-stained slides to predict the 5-year overall survival in clear-cell renal cell carcinoma. The mean accuracy after 10-fold cross-validation was 0.655, with an AUC of 0.700.…”

Spatial attention wavelon network (SpAWN) for survival-based risk stratification in kidney cancers via CT

“…Prognostic models for overall survival have been previously developed for kidney cancers using tabulated clinical information, 3,4 genomics or proteomics, 5 and histopathological imaging data. 6,7 A key alternative may be non-invasive diagnostic CT imaging, which is routinely used by clinicians to manually determine the extent, size, and location of tumors in the kidney, 8 but which suffers from significant inter-reader variability 9 and thus limited prognostic ability 10 To our knowledge, there have been no significant efforts in developing prognostic CNN models for kidney cancers using radiographic CT imaging alone.…”