التفاصيل البيبلوغرافية
| العنوان: |
Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. |
| المؤلفون: |
Wessels, Frederik, Schmitt, Max, Krieghoff‐Henning, Eva, Jutzi, Tanja, Worst, Thomas S., Waldbillig, Frank, Neuberger, Manuel, Maron, Roman C., Steeg, Matthias, Gaiser, Timo, Hekler, Achim, Utikal, Jochen S., von Kalle, Christof, Fröhling, Stefan, Michel, Maurice S., Nuhn, Philipp, Brinker, Titus J. |
| المصدر: |
BJU International; Sep2021, Vol. 128 Issue 3, p352-360, 9p |
| مصطلحات موضوعية: |
LYMPHATIC metastasis, DEEP learning, GLEASON grading system, PROSTATECTOMY, HISTOLOGY, RECEIVER operating characteristic curves, CONVOLUTIONAL neural networks, AGE factors in cancer, SIGNAL convolution |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of BJU International is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder