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Gastric Cancer
Erschienen in:

06.08.2022 | Original Article

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study

verfasst von: Jiayi Zhang, Yanfen Cui, Kaikai Wei, Zhenhui Li, Dandan Li, Ruirui Song, Jialiang Ren, Xin Gao, Xiaotang Yang

Erschienen in: Gastric Cancer | Ausgabe 6/2022

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Abstract

Background

Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients.

Methods

A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC).

Results

The DL model achieved AUCs of 0.808 (95% CI 0.724–0.893), 0.755 (95% CI 0.660–0.850), and 0.752 (95% CI 0.678–0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05).

Conclusions

A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.
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Literatur
1.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2018;68(6):394–424.PubMed
2.
Roukos D. Current status and future perspectives in gastric cancer management. Cancer Treat Rev. 2000;26(4):243–55.CrossRef
3.
Ang J, Hu L, Huang P-T, Wu J-X, Huang L-N, Cao C-H, Zheng Y-X, Chen L. Contrast-enhanced ultrasonography assessment of gastric cancer response to neoadjuvant chemotherapy. World J Gastroenterol WJG. 2012;18(47):7026.CrossRef
4.
Wang X-Z, Zeng Z-Y, Ye X, Sun J, Zhang Z-M, Kang W-M. Interpretation of the development of neoadjuvant therapy for gastric cancer based on the vicissitudes of the NCCN guidelines. World J Gast Oncol. 2020;12(1):37.CrossRef
5.
Fu J, Tang L, Li Z-Y, Li X-T, Zhu H-F, Sun Y-S, Ji J-F. Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy. Eur J Radiol. 2020;128: 108974.CrossRef
6.
Li Z, Zhang D, Dai Y, Dong J, Wu L, Li Y, Cheng Z, Ding Y, Liu Z. Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res. 2018;30(4):406.CrossRef
7.
Lauridsen CA, Falletin E, Hansen ML, Law I, Federspiel B, Bæksgaard L, Svendsen LB, Nielsen MB. Computed Tomography (CT) Perfusion as an early prognostic marker for treatment response to neoadjuvant chemotherapy in gastroesophageal junction cancer and gastric cancer-a prospective study. PLoS ONE. 2014;9:1–10.
8.
Virendra K, Yuhua G, Satrajit B. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.CrossRef
9.
10.
Wang W, Peng Y, Feng X, Zhao Y, Seeruttun SR, Zhang J, Cheng Z, Li Y, Liu Z, Zhou Z. Development and validation of a computed tomography-based radiomics signature to predict response to neoadjuvant chemotherapy for locally advanced gastric cancer. JAMA Netw Open. 2021;4(8):e2121143–e2121143.CrossRef
11.
Sun K-Y, Hu H-T, Chen S-L, Ye J-N, Li G-H, Chen L-D, Peng J-J, Feng S-T, Yuan Y-J, Hou X. CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer. BMC Cancer. 2020;20:1–11.CrossRef
12.
Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis C-A, Gaiser T, Marx A, Valous NA, Ferber D. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16(1): e1002730.CrossRef
13.
Peng H, Dong D, Fang M-J, Li L, Tang L-L, Chen L, Li W-F, Mao Y-P, Fan W, Liu L-Z. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2019;25(14):4271–9.CrossRef
14.
Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Vega JEV, Brat DJ, Cooper LA. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci. 2018;115(13):E2970–9.CrossRef
15.
Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, Li N, Chen H, Yu J, Xie Y. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw Open. 2021;4(1):e2032269–e2032269.CrossRef
16.
Jin C, Jiang Y, Yu H, Wang W, Li B, Chen C, Yuan Q, Hu Y, Xu Y, Zhou Z. Deep learning analysis of the primary tumour and the prediction of lymph node metastases in gastric cancer. Br J Surg. 2021;108(5):542–9.CrossRef
17.
Gao Y, Zhang Z-D, Li S, Guo Y-T, Wu Q-Y, Liu S-H, Yang S-J, Ding L, Zhao B-C, Li S. Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer. Chin Med J. 2019;132(23):2804.CrossRef
18.
Amin MBES, Greene F, et al. AJCC Cancer Staging Manual. 8th ed. New York: Springer; 2017.CrossRef
19.
20.
21.
22.
23.
24.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 770–778
25.
Dao T, Gu A, Ratner AJ, Smith V, Sa CD, Ré C. A Kernel Theory of Modern Data Augmentation. PMLR, 2019. pp 1528–1537
26.
Pan SJ, Qiang Y. A Survey on Transfer Learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–59.CrossRef
27.
Steiner B, Devito Z, Chintala S, Gross S, Paszke A, Massa F, Lerer A, Chanan G, Lin Z, Yang E. PyTorch: an imperative style, high-performance deep learning library. Neural information processing systems; 2019. p. 8026–37.
28.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 2921–2929
29.
Sauerbrei W, Boulesteix A-L, Binder H. Stability investigations of multivariable regression models derived from low-and high-dimensional data. J Biopharm Stat. 2011;21(6):1206–31.CrossRef
30.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36.CrossRef
31.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRef
32.
Cheong J-H, Yang H-K, Kim H, Kim WH, Kim Y-W, Kook M-C, Park Y-K, Kim H-H, Lee HS, Lee KH. Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis. Lancet Oncol. 2018;19(5):629–38.CrossRef
33.
Wu J, Li B, Sun X, Cao G, Rubin DL, Napel S, Ikeda DM, Kurian AW, Li R. Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with dysregulated signaling pathways and poor survival in breast cancer. Radiology. 2017;285(2):401–13.CrossRef
34.
Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, Kurian AW, Daniel BL, Li R. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology. 2018;288(1):26–35.CrossRef
35.
Feng F, Tian Y, Xu G, Liu Z, Liu S, Zheng G, Guo M, Lian X, Fan D, Zhang H. Diagnostic and prognostic value of CEA, CA19–9, AFP and CA125 for early gastric cancer. BMC Cancer. 2017;17(1):737.CrossRef
36.
Chen XZ, Zhang WK, Yang K, Wang LL, Liu J, Wang L, Hu JK, Zhang B, Chen ZX, Chen JP. Correlation between serum CA724 and gastric cancer: multiple analyses based on Chinese population. Mol Biol Rep. 2012;39(9):9031–9.CrossRef
37.
38.
Takekazu Y, Shunkichi K, Akira K, Koichi K, Takayoshi H, Norishige T, Masakazu M. Tumor markers CEA, CA19-9 and CA125 in monitoring of response to systemic chemotherapy in patients with advanced gastric cancer. Jpn J Clin Oncol. 1999;11:550–5.
39.
Sun Z, Zhang N. Clinical evaluation of CEA, CA19-9, CA72-4 and CA125 in gastric cancer patients with neoadjuvant chemotherapy. World J Surg Oncol. 2014;12(1):397.CrossRef
Metadaten
Titel
Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study
verfasst von
Jiayi Zhang
Yanfen Cui
Kaikai Wei
Zhenhui Li
Dandan Li
Ruirui Song
Jialiang Ren
Xin Gao
Xiaotang Yang
Publikationsdatum
06.08.2022
Verlag
Springer Nature Singapore
Erschienen in
Gastric Cancer / Ausgabe 6/2022
Print ISSN: 1436-3291
Elektronische ISSN: 1436-3305
DOI
https://doi.org/10.1007/s10120-022-01328-3

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