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Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer
Author(s) -
Wessels Frederik,
Schmitt Max,
KrieghoffHenning Eva,
Jutzi Tanja,
Worst Thomas S.,
Waldbillig Frank,
Neuberger Manuel,
Maron Roman C.,
Steeg Matthias,
Gaiser Timo,
Hekler Achim,
Utikal Jochen S.,
Kalle Christof,
Fröhling Stefan,
Michel Maurice S.,
Nuhn Philipp,
Brinker Titus J.
Publication year - 2021
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/bju.15386
Subject(s) - medicine , confidence interval , perineural invasion , prostate cancer , receiver operating characteristic , lymphovascular invasion , prostatectomy , odds ratio , nomogram , lymph node , oncology , metastasis , cancer , urology , radiology
Objective To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors. Patients and Methods Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status. Results With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678–0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05–62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77–56.41%) and 69.65% (95% CI 68.21–71.1%), respectively. These results were confirmed via cross‐validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02–1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96–35.7; P < 0.001) proved to be independent predictors for LNM. Conclusion In our present study, CNN‐based image analyses showed promising results as a potential novel low‐cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.