影像组学在乳腺癌新辅助化疗疗效预测的研究进展

[2]Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249.

[3]Korde L A, Somerfield M R, Carey L A, et al. Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO guideline[J]. Journal of Clinical Oncology, 2021, 39(13): 1485-1505.

[4]Masuda N, Lee S J, Ohtani S, et al. Adjuvant capecitabine for breast cancer after preoperative chemotherapy[J]. New England Journal of Medicine, 2017, 376(22): 2147-2159.

[5]Golshan M, Cirrincione C T, Sikov W M, et al. Impact of neoadjuvant chemotherapy in stage II-III triple negative breast cancer on eligibility for breast-conserving surgery and breast conservation rates: Surgical results from CALGB 40603 (alliance)[J]. Annals of Surgery, 2015, 262(3): 434-439.

[6]Spring L M, Fell G, Arfe A, et al. Pathologic Complete Response after Neoadjuvant Chemotherapy and Impact on Breast Cancer Recurrence and Survival: A Comprehensive Meta-analysis[J]. Clinical Cancer Research, 2020, 26(12): 2838-2848.

[7]邵志敏, 吴炅, 江泽飞, 等. 中国乳腺癌新辅助治疗专家共识(2022年版)[J]. 中国癌症杂志, 2022, 32(1): 80-89.

[8]Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis[J]. The Lancet, 2014, 384(9938): 164-172.

[9]Liu W, Chen W, Zhang X, et al. Higher efficacy and reduced adverse reactions in neoadjuvant chemotherapy for breast cancer by using pegylated liposomal doxorubicin compared with pirarubicin[J]. Scientific Reports, 2021, 11(1): 199.

[10]Mayerhoefer M E, Materka A, Langs G, et al. Introduction to radiomics[J]. Journal of Nuclear Medicine, 2020, 61(4): 488-495.

[11]Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis[J]. European Journal of Cancer (oxford, England: 1990), 2012, 48(4): 441-446.

[12]Chen W, Zhang W, Chen X, et al. Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting[J]. Clinical and Translational Oncology, 2024, 26(8): 1944-1955.

[13]Aerts H J W L, Velazquez E R, Leijenaar R T H, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nature Communications, 2014, 5(1): 4006.

[14]Dong D, Tang L, Li Z Y, et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer[J]. Annals of Oncology, 2019, 30(3): 431-438.

[15]Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: Making personalized medicine a reality via routine imaging[J]. Medicinal Research Reviews, 2022, 42(1): 426-440.

[16]Gillies R J, Kinahan P E, Hricak H. Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577.

[17]Scapicchio C, Gabelloni M, Barucci A, et al. A deep look into radiomics[J]. La radiologia medica, 2021, 126(10): 1296-1311.

[18]Chan H P, Samala R K, Hadjiiski L M, et al. Deep learning in medical image analysis[J]. Advances in Experimental Medicine and Biology, 2020, 1213: 3-21.

[19]Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Translational Vision Science & Technology, 2020, 9(2): 14.

[20]Duggento A, Conti A, Mauriello A, et al. Deep computational pathology in breast cancer[J]. Seminars in Cancer Biology, 2021, 72: 226-237.

[21]Moghadas-Dastjerdi H, Rahman S E T H, Sannachi L, et al. Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning[J]. Translational Oncology, 2021, 14(10): 101183.

[22]Huang X, Mai J, Huang Y, et al. Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT[J]. Clinical Breast Cancer, 2021, 21(4): e388-e401.

[23]Saednia K, Lagree A, Alera M A, et al. Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies[J]. Scientific Reports, 2022, 12(1): 9690.

[24]Kolios C, Sannachi L, Dasgupta A, et al. MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer[J]. Oncotarget, 2021, 12(14): 1354-1365.

[25]Fusco R, Granata V, Maio F, et al. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data[J]. European Radiology Experimental, 2020, 4: 8.

[26]Chen X, Chen X, Yang J, et al. Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients[J]. Journal of Computer Assisted Tomography, 2020, 44(2): 275-283.

[27]Liu Z, Li Z, Qu J, et al. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study[J]. Clinical Cancer Research, 2019, 25(12): 3538-3547.

[28]Lim C H, Choi J Y, Choi J H, et al. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer[J]. Cancers, 2023, 15(15): 3842.

[29]Li P, Wang X, Xu C, et al. 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2020, 47(5): 1116-1126.

[30]Antunovic L, De Sanctis R, Cozzi L, et al. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2019, 46(7): 1468-1477.

[31]Luo J, Zhou Z, Yang Z, et al. The Value of 18F-FDG PET/CT Imaging Combined With Pretherapeutic Ki67 for Early Prediction of Pathologic Response After Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer[J]. Medicine, 2016, 95(8): e2914.

[32]Yang M, Liu H, Dai Q, et al. Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer[J]. Frontiers in Oncology, 2022, 12: 748008.

[33]Liu J, Leng X, Liu W, et al. An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer[J]. Frontiers in Oncology, 2024, 14: 1285511.

[34]Sannachi L, Gangeh M, Tadayyon H, et al. Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features[J]. PLOS ONE, 2018, 13(1): e0189634.

[35]DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi-institutional study[J]. Cancer Medicine, 2020, 9(16): 5798-5806.

[36]Bhardwaj D, Dasgupta A, DiCenzo D, et al. Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer[J]. Cancers, 2022, 14(5): 1247.

[37]Huang J X, Shi J, Ding S S, et al. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer[J]. Academic Radiology, 2023, 30: S50-S61.

[38]Wan C feng, Jiang Z yun, Wang Y qun, et al. Radiomics of multimodal ultrasound for early prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer[J]. Academic Radiology, 2024: S1076633224008559.

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