Dataset Information

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study.

ABSTRACT:

Background

Accurate prediction of lymph node metastasis (LNM) status in patients with muscle-invasive bladder cancer (MIBC) before radical cystectomy can guide the use of neoadjuvant chemotherapy and the extent of pelvic lymph node dissection. We aimed to develop and validate a weakly-supervised deep learning model to predict LNM status from digitized histopathological slides in MIBC.

Methods

We trained a multiple instance learning model with an attention mechanism (namely SBLNP) from a cohort of 323 patients in the TCGA cohort. In parallel, we collected corresponding clinical information to construct a logistic regression model. Subsequently, the score predicted by the SBLNP was incorporated into the logistic regression model. In total, 417 WSIs from 139 patients in the RHWU cohort and 230 WSIs from 78 patients in the PHHC cohort were used as independent external validation sets.

Results

In the TCGA cohort, the SBLNP achieved an AUROC of 0.811 (95% confidence interval [CI], 0.771-0.855), the clinical classifier achieved an AUROC of 0.697 (95% CI, 0.661-0.728) and the combined classifier yielded an improvement to 0.864 (95% CI, 0.827-0.906). Encouragingly, the SBLNP still maintained high performance in the RHWU cohort and PHHC cohort, with an AUROC of 0.762 (95% CI, 0.725-0.801) and 0.746 (95% CI, 0.687-0.799), respectively. Moreover, the interpretability of SBLNP identified stroma with lymphocytic inflammation as a key feature of predicting LNM presence.

Conclusions

Our proposed weakly-supervised deep learning model can predict the LNM status of MIBC patients from routine WSIs, demonstrating decent generalization performance and holding promise for clinical implementation.

SUBMITTER: Zheng Q

PROVIDER: S-EPMC10251851 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study.

Zheng Qingyuan Q Jian Jun J Wang Jingsong J Wang Kai K Fan Junjie J Xu Huazhen H Ni Xinmiao X Yang Song S Yuan Jingping J Wu Jiejun J Jiao Panpan P Yang Rui R Chen Zhiyuan Z Liu Xiuheng X Wang Lei L

Cancers 20230531 11

<h4>Background</h4>Accurate prediction of lymph node metastasis (LNM) status in patients with muscle-invasive bladder cancer (MIBC) before radical cystectomy can guide the use of neoadjuvant chemotherapy and the extent of pelvic lymph node dissection. We aimed to develop and validate a weakly-supervised deep learning model to predict LNM status from digitized histopathological slides in MIBC.<h4>Methods</h4>We trained a multiple instance learning model with an attention mechanism (namely SBLNP) ...[more]

PMID: 37296961

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Project description:BACKGROUND:For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS:We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS:In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Project description:PurposePelvic lymph node metastases (ypN+) after multiagent neoadjuvant chemotherapy (NAC) is a poor prognostic sign in nonmetastatic muscle-invasive bladder cancer (nmMIBC). We sought to create a nomogram predicting probability of ypN+ after NAC for cN0 nmMIBC and determine association with overall survival (OS).Methods and materialsWe reviewed the National Cancer Database for patients with cT2-4N0M0 urothelial carcinoma of the bladder receiving multiagent NAC and surgery from 2004 to 2020. Following a data split, univariate logistic regression identified variables associated with ypN+ at P < .05. Eligible variables were used for multivariate logistic regression and nomogram generation. A threshold for 95% sensitivity defined high- and low-risk groups for ypN+. Fine-Gray models assessed ypN+ risk group and OS, accounting for competing risks of surgical mortality.ResultsA total of 6194 patients were identified with a median follow-up of 39.5 months (interquartile range [IQR], 20.5-67.2 months). Most patients had high-grade (97.7%) cT2 disease (70.8%) with nonpapillary urothelial histology (67.3%) and initiated NAC at a median of 41.0 days after diagnosis (IQR, 28.0-59.0 days).The nomogram included age in decades (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.87-1.03; P = .172), weeks from diagnosis to NAC (OR, 1.02; 95% CI, 1.01-1.04; P = .004), nonpapillary histology (OR, 1.17; 95% CI, 0.99-1.39; P = .068), and clinical T-stage. Within the testing cohort, ypN+ was found in 392 (22.8%) high-risk and 12 (8.0%) low-risk patients (P < .001), with median OS of 36.1 and 74.0 months, respectively (P < .001). High-risk patients had worse OS despite competing risks of 30-day (subdistribution hazard ratio [SHR], 1.80; 95% CI, 1.49-2.18; P < .001) and 90-day surgical mortality (SHR, 1.68; 95% CI, 1.39-2.04; P < .001).ConclusionsThis is the first study to provide a tool for predicting ypN+ and prognosticate worse OS in primarily high-grade nmMIBC and could select patients for alternative neoadjuvant therapy and facilitate future study.

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Dataset Information

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study.

Background

Methods

Results

Conclusions

Publications

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study.

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