18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30. https://doi.org/10.3322/caac.21332.

Article  PubMed  Google Scholar 

Gradishar WJ, Anderson BO, Abraham J, Aft R, Agnese D, Allison KH, et al. Breast cancer, version 3.2020, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2020;18:452–78. https://doi.org/10.6004/jnccn.2020.0016.

Article  CAS  PubMed  Google Scholar 

Fisher B, Bryant J, Wolmark N, Mamounas E, Brown A, Fisher ER, et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol. 1998;16:2672–85. https://doi.org/10.1200/JCO.1998.16.8.2672.

Article  CAS  PubMed  Google Scholar 

Smith IC, Heys SD, Hutcheon AW, Miller ID, Payne S, Gilbert FJ, et al. Neoadjuvant chemotherapy in breast cancer: significantly enhanced response with docetaxel. J Clin Oncol. 2002;20:1456–66. https://doi.org/10.1200/JCO.2002.20.6.1456.

Article  CAS  PubMed  Google Scholar 

Pierga JY, Mouret E, Dieras V, Laurence V, Beuzeboc P, Dorval T, et al. Prognostic value of persistent node involvement after neoadjuvant chemotherapy in patients with operable breast cancer. Br J Cancer. 2000;83:1480–7. https://doi.org/10.1054/bjoc.2000.1461.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liedtke C, Mazouni C, Hess KR, Andre F, Tordai A, Mejia JA, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008;26:1275–81. https://doi.org/10.1200/JCO.2007.14.4147.

Article  PubMed  Google Scholar 

Kong X, Moran MS, Zhang N, Haffty B, Yang Q. Meta-analysis confirms achieving pathological complete response after neoadjuvant chemotherapy predicts favourable prognosis for breast cancer patients. Eur J Cancer. 2011;47:2084–90. https://doi.org/10.1016/j.ejca.2011.06.014.

Article  PubMed  Google Scholar 

Cianfrocca M, Goldstein LJ. Prognostic and predictive factors in early-stage breast cancer. Oncologist. 2004;9:606–16. https://doi.org/10.1634/theoncologist.9-6-606.

Article  PubMed  Google Scholar 

Symmans WF, Wei C, Gould R, Yu X, Zhang Y, Liu M, et al. Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype. J Clin Oncol. 2017;35:1049–60. https://doi.org/10.1200/JCO.2015.63.1010.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pinard C, Debled M, Ben Rejeb H, Velasco V, Tunon de Lara C, Hoppe S, et al. Residual cancer burden index and tumor-infiltrating lymphocyte subtypes in triple-negative breast cancer after neoadjuvant chemotherapy. Breast Cancer Res Treat. 2020;179:11–23. https://doi.org/10.1007/s10549-019-05437-z.

Article  CAS  PubMed  Google Scholar 

Podoloff DA, Advani RH, Allred C, Benson AB 3rd, Brown E, Burstein HJ, et al. NCCN task force report: positron emission tomography (PET)/computed tomography (CT) scanning in cancer. J Natl Compr Canc Netw. 2007;5(1):S1-22 (quiz S3–2).

Article  PubMed  Google Scholar 

Hatt M, Visvikis D, Albarghach NM, Tixier F, Pradier O, Cheze-le RC. Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:1191–202. https://doi.org/10.1007/s00259-011-1755-7.

Article  PubMed  Google Scholar 

Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1):122S-S150. https://doi.org/10.2967/jnumed.108.057307.

Article  CAS  PubMed  Google Scholar 

Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26. https://doi.org/10.2967/jnumed.112.107375.

Article  PubMed  Google Scholar 

Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40. https://doi.org/10.1007/s00259-012-2247-0.

Article  PubMed  Google Scholar 

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6. https://doi.org/10.1016/j.ejca.2011.11.036.

Article  PubMed  PubMed Central  Google Scholar 

Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuze S, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28:1191–206. https://doi.org/10.1093/annonc/mdx034.

Article  CAS  PubMed  Google Scholar 

Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–5. https://doi.org/10.1056/NEJMp1500523.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cook GJR, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V. Radiomics in PET: principles and applications. Clin Transl Imaging. 2014;2:269–76. https://doi.org/10.1007/s40336-014-0064-0.

Article  Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. https://doi.org/10.1038/nature14539.

Article  CAS  PubMed  Google Scholar 

Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475:131–8. https://doi.org/10.1007/s00428-019-02594-w.

Article  CAS  PubMed  Google Scholar 

Li C, Xue D, Hu Z, Chen H, Yao Y, Zhang Y, et al. A survey for breast histopathology image analysis using classical and deep neural networks. In: Pietka E, Badura P, Kawa J, Wieclawek W, editors., et al., Information technology in biomedicine. Cham: Springer International Publishing; 2019. p. 222–33.

Chapter  Google Scholar 

Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. 2016.

Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23. https://doi.org/10.1093/annonc/mdt303.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Siavashpour Z, Aghamiri MR, Jaberi R, Dehghan-Manshadi HR, Sedaghat M, Kirisits C. Evaluating the utility of “3D Slicer” as a fast and independent tool to assess intrafractional organ dose variations in gynecological brachytherapy. Brachytherapy. 2016;15:514–23. https://doi.org/10.1016/j.brachy.2016.03.009.

Article  PubMed  Google Scholar 

Zwanenburg A, Leger S, Vallières M, Lck SJR, Oncology. Image biomarker standardisation initiative. 2016.

Asano Y, Kashiwagi S, Goto W, Takada K, Takahashi K, Hatano T, et al. Prediction of survival after neoadjuvant chemotherapy for breast cancer by evaluation of tumor-infiltrating lymphocytes and residual cancer burden. BMC Cancer. 2017;17:888. https://doi.org/10.1186/s12885-017-3927-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hamy AS, Darrigues L, Laas E, De Croze D, Topciu L, Lam GT, et al. Prognostic value of the Residual Cancer Burden index according to breast cancer subtype: Validation on a cohort of BC patients treated by neoadjuvant chemotherapy. PLoS ONE. 2020;15: e0234191. https://doi.org/10.1371/journal.pone.0234191.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Qu YH, Long N, Ran C, Sun J. The correlation of (18)F-FDG PET/CT metabolic parameters, clinicopathological factors, and prognosis in breast cancer. Clin Transl Oncol. 2021;23:620–7. https://doi.org/10.1007/s12094-020-02457-w.

Article  CAS  PubMed  Google Scholar 

Aogi K, Kadoya T, Sugawara Y, Kiyoto S, Shigematsu H, Masumoto N, et al. Utility of (18)F FDG-PET/CT for predicting prognosis of luminal-type breast cancer. Breast Cancer Res Treat. 2015;150:209–17. https://doi.org/10.1007/s10549-015-3303-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Higuchi T, Nishimukai A, Ozawa H, Fujimoto Y, Yanai A, Miyagawa Y, et al. Prognostic significance of preoperative (18)F-FDG PET/CT for breast cancer subtypes. Breast. 2016;30:5–12. https://doi.org/10.1016/j.breast.2016.08.003.

Article  PubMed  Google Scholar 

Seban RD, Rouzier R, Latouche A, Deleval N, Guinebretiere JM, Buvat I, et al. Total metabolic tumor volume and spleen metabolism on baseline [18F]-FDG PET/CT as independent prognostic biomarkers of recurrence in resected breast cancer. Eur J Nucl Med Mol Imaging. 2021;48:3560–70. https://doi.org/10.1007/s00259-021-05322-2.

Article  CAS  PubMed  Google Scholar 

Groheux D, Sanna A, Majdoub M, de Cremoux P, Giacchetti S, Teixeira L, et al. Baseline tumor 18F-FDG uptake and modifications after 2 cycles of neoadjuvant chemotherapy are prognostic of outcome in ER+/HER2- breast cancer. J Nucl Med. 2015;56:824–31. https://doi.org/10.2967/jnumed.115.154138.

Article  CAS  PubMed  Google Scholar 

Garcia Vicente AM, Soriano Castrejon A, Lopez-Fidalgo JF, Amo-Salas M, Munoz Sanchez Mdel M, Alvarez Cabellos R, et al. Basal (1)(8)F-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography as a prognostic biomarker in patients with locally advanced breast cancer. Eur J Nucl Med Mol Imaging. 2015;42:1804–13. https://doi.org/10.1007/s00259-015-3102-x.

Article  CAS  PubMed  Google Scholar 

Choi WH, Han EJ, Choi EK, Chae BJ, Park YG, et al. The prognostic value of (18)F-FDG PET/CT for early recurrence in opera

留言 (0)

沒有登入
gif