Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660.
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 Cancer J Clin. 2018;68:394–424. https://doi.org/10.3322/caac.21492.
Rose J, Puckett Y. Breast Reconstruction Free Flaps. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.; 2022.
Leitlinienprogramm Onkologie (Deutsche Krebsgesellschaft DK, AWMF). S3-Leitlinie Früherkennung, Diagnose, Therapie und Nachsorge des Mammakarzinoms. 2021; Version 4.4. AWMF Registernummer: 032-045OL. http://www.leitlinienprogramm-onkologie.de/leitlinien/mammakarzinom/. (abgerufen am: 11.11.2023)
Bruckmann NM, Morawitz J, Fendler WP, Ruckhäberle E, Bittner AK, Giesel FL, et al. A role of PET/MR in breast cancer? Semin Nucl Med. 2022. https://doi.org/10.1053/j.semnuclmed.2022.01.003.
Murthy V, Sonni I, Jariwala N, Juarez R, Reiter RE, Raman SS, et al. The role of PSMA PET/CT and PET/MRI in the initial staging of prostate cancer. Eur Urol Focus. 2021;7:258–66. https://doi.org/10.1016/j.euf.2021.01.016.
Heacock L, Weissbrot J, Raad R, Campbell N, Friedman KP, Ponzo F, et al. PET/MRI for the evaluation of patients with lymphoma: initial observations. AJR Am J Roentgenol. 2015;204:842–8. https://doi.org/10.2214/AJR.14.13181.
Kirchner J, Grueneisen J, Martin O, Oehmigen M, Quick HH, Bittner A-K, et al. Local and whole-body staging in patients with primary breast cancer: a comparison of one-step to two-step staging utilizing 18F-FDG-PET/MRI. Eur J Nucl Med Mol Imaging. 2018;45:2328–37. https://doi.org/10.1007/s00259-018-4102-4.
Bruckmann NM, Kirchner J, Morawitz J, Umutlu L, Herrmann K, Bittner AK, et al. Prospective comparison of CT and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients: initial results. PLoS ONE. 2021;16: e0260804. https://doi.org/10.1371/journal.pone.0260804.
Bruckmann NM, Kirchner J, Umutlu L, Fendler WP, Seifert R, Herrmann K, et al. Prospective comparison of the diagnostic accuracy of 18F-FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients. Eur Radiol. 2021;31:8714–24. https://doi.org/10.1007/s00330-021-07956-0.
Cardoso F, Paluch-Shimon S, Senkus E, Curigliano G, Aapro MS, André F, et al. 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5). Ann Oncol. 2020;31:1623–49. https://doi.org/10.1016/j.annonc.2020.09.010.
Mougalian SS, Hernandez M, Lei X, Lynch S, Kuerer HM, Symmans WF, et al. Ten-year outcomes of patients with breast cancer with cytologically confirmed axillary lymph node metastases and pathologic complete response after primary systemic chemotherapy. JAMA Oncol. 2016;2:508–16. https://doi.org/10.1001/jamaoncol.2015.4935.
Fayanju OM, Ren Y, Thomas SM, Greenup RA, Plichta JK, Rosenberger LH, et al. The clinical significance of breast-only and node-only pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT): a review of 20,000 breast cancer patients in the National Cancer Data Base (NCDB). Ann Surg. 2018;268:591–601. https://doi.org/10.1097/sla.0000000000002953.
Gu YL, Pan SM, Ren J, Yang ZX, Jiang GQ. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: a meta-analysis. Clin Breast Cancer. 2017;17:245–55. https://doi.org/10.1016/j.clbc.2016.12.010.
Urso L, Evangelista L, Alongi P, Quartuccio N, Cittanti C, Rambaldi I, et al. The value of semiquantitative parameters derived from (18)F-FDG PET/CT for predicting response to neoadjuvant chemotherapy in a cohort of patients with different molecular subtypes of breast cancer. Cancers (Basel). 2022;14(23):5869. https://doi.org/10.3390/cancers14235869.
Park SH, Moon WK, Cho N, Chang JM, Im S-A, Park I, et al. Comparison of diffusion-weighted MR imaging and FDG PET/CT to predict pathological complete response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol. 2012;22:18–25.
Morawitz J, Sigl B, Rubbert C, Bruckmann NM, Dietzel F, Häberle LJ, et al. Clinical decision support for axillary lymph node staging in newly diagnosed breast cancer patients based on (18)F-FDG PET/MRI and machine learning. J Nucl Med. 2023;64:304–11. https://doi.org/10.2967/jnumed.122.264138.
Pehrson LM, Nielsen MB, Ammitzbøl LC. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review. Diagnostics. 2019;9:29.
Rubbert C, Wolf L, Turowski B, Hedderich DM, Gaser C, Dahnke R, et al. Impact of defacing on automated brain atrophy estimation. Insights Imaging. 2022;13:54. https://doi.org/10.1186/s13244-022-01195-7.
Morawitz J, Bruckmann N-M, Dietzel F, Ullrich T, Bittner A-K, Hoffmann O, et al. Comparison of nodal staging between CT, MRI, and [(18)F]-FDG PET/MRI in patients with newly diagnosed breast cancer. Eur J Nucl Med Mol Imaging. 2022;49:992–1001. https://doi.org/10.1007/s00259-021-05502-0.
Atallah D, Moubarak M, Arab W, El Kassis N, Chahine G, Salem C. MRI-based predictive factors of axillary lymph node status in breast cancer. Breast J. 2020;26:2177–82. https://doi.org/10.1111/tbj.14089.
Association WM. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–4. https://doi.org/10.1001/jama.2013.281053.
Oehmigen M, Lindemann ME, Lanz T, Kinner S, Quick HH. Integrated PET/MR breast cancer imaging: attenuation correction and implementation of a 16-channel RF coil. Med Phys. 2016;43:4808. https://doi.org/10.1118/1.4959546.
Martinez-Möller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd’hotel C, Ziegler SI, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50:520–6. https://doi.org/10.2967/jnumed.108.054726.
Paulus DH, Quick HH, Geppert C, Fenchel M, Zhan Y, Hermosillo G, et al. Whole-body PET/MR imaging: quantitative evaluation of a novel model-based MR attenuation correction method including bone. J Nucl Med. 2015;56:1061–6. https://doi.org/10.2967/jnumed.115.156000.
Lindemann ME, Oehmigen M, Blumhagen JO, Gratz M, Quick HH. MR-based truncation and attenuation correction in integrated PET/MR hybrid imaging using HUGE with continuous table motion. Med Phys. 2017;44:4559–72. https://doi.org/10.1002/mp.12449.
Sinn HP, Schmid H, Junkermann H, Huober J, Leppien G, Kaufmann M, et al. Histologic regression of breast cancer after primary (neoadjuvant) chemotherapy. Geburtshilfe Frauenheilkd. 1994;54:552–8. https://doi.org/10.1055/s-2007-1022338.
Du Bois D, Du Bois EF. Clinical calorimetry: tenth paper a formula to estimate the approximate surface area if height and weight be known. Arch Intern Med. 1916;XVII:863–71. https://doi.org/10.1001/archinte.1916.00080130010002.
Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13:281–305.
Hurria A, Soto-Perez-de-Celis E, Allred JB, Cohen HJ, Arsenyan A, Ballman K, et al. Functional decline and resilience in older women receiving adjuvant chemotherapy for breast cancer. J Am Geriatr Soc. 2019;67:920–7. https://doi.org/10.1111/jgs.15493.
Heil J, Kümmel S, Schaefgen B, Paepke S, Thomssen C, Rauch G, et al. Diagnosis of pathological complete response to neoadjuvant chemotherapy in breast cancer by minimal invasive biopsy techniques. Br J Cancer. 2015;113:1565–70. https://doi.org/10.1038/bjc.2015.381.
Del Prete S, Caraglia M, Luce A, Montella L, Galizia G, Sperlongano P, et al. Clinical and pathological factors predictive of response to neoadjuvant chemotherapy in breast cancer: a single center experience. Oncol Lett. 2019;18:3873–9. https://doi.org/10.3892/ol.2019.10729.
Wang L, Zhang S, Wang X. The Metabolic Mechanisms of Breast Cancer Metastasis. Front Oncol. 2020;10: 602416. https://doi.org/10.3389/fonc.2020.602416.
Catalano OA, Horn GL, Signore A, Iannace C, Lepore M, Vangel M, et al. PET/MR in invasive ductal breast cancer: correlation between imaging markers and histological phenotype. Br J Cancer. 2017;116:893–902. https://doi.org/10.1038/bjc.2017.26.
Jannusch K, Bittner A-K, Bruckmann NM, Morawitz J, Stieglitz C, Dietzel F, et al. Correlation between imaging markers derived from pet/mri and invasive acquired biomarkers in newly diagnosed breast cancer. Cancers. 2023;15:1651.
Choi JH, Kim H-A, Kim W, Lim I, Lee I, Byun BH, et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep. 2020;10:21149. https://doi.org/10.1038/s41598-020-77875-5.
Parvandeh S, Yeh H-W, Paulus MP, McKinney BA. Consensus features nested cross-validation. Bioinformatics. 2020;36:3093–8. https://doi.org/10.1093/bioinformatics/btaa046.
Tsamardinos I, Rakhshani A, Lagani V. Performance-estimation properties of cross-validation-based protocols with simultaneous hyper-parameter optimization. Int J Artif Intell Tools. 2015;24:1540023. https://doi.org/10.1142/s0218213015400230.
Wainer J, Cawley G. Nested cross-validation when selecting classifiers is overzealous for most practical applications. Expert Syst Appl. 2021;182: 115222. https://doi.org/10.1016/j.eswa.2021.115222.
Varoquaux G, Cheplygina V. Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit Med. 2022;5:48. https://doi.org/10.1038/s41746-022-00592-y.
Tahmassebi A, Wengert GJ, Helbich TH, Bago-Horvath Z, Alaei S, Bartsch R, et al. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol. 2019;54:110–7. https://doi.org/10.1097/rli.0000000000000518.
Syed A, Adam R, Ren T, Lu J, Maldjian T, Duong TQ. Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer. PLoS ONE. 2023;18: e0280320. https://doi.org/10.1371/journal.pone.0280320.
