A Meta-Analysis Study Of Quantitative Intravoxel Incoherent Motion (DWI) And Dynamic Contrast-Enhanced MRI To Evaluate Neoadjuvant Chemotherapy In Breast Cancer
Main Article Content
Keywords
Early prediction, DCE-MRI, IVIM , Neoadjuvant chemotherapy, Breast cancer.
Abstract
Objective: The aim of the current study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel coherent motion (DWI) in predicting breast cancer patient response to neoadjuvant chemotherapy (NAC).
Materials and Methods: We searched international databases including PubMed, Medline, Embase, and Science direct with appropriate keywords. The variance of each studies were calculated that assessed the use of Non-Gaussian DWI model (Intravoxel Incoherent Motion; perfusion fraction ‘f’ ; real diffusivity ‘D’ and pseudo-diffusivity ‘D*’) and dynamic contrast-enhanced of prediction of response of breast cancer. Pooling the sensitivity, specificity, and area under the curve were used to organize and summarize the studies. And the data were analyzed using STATA version 14. Finally, the results of the studies were entered into the random-effects meta-analysis.
Results: twenty one studies comprising 2161 patients were involved in the present study. The sensitivity and specificity of DCE-MRI were 0.693 (95% CI 0.560-0.826), and 0.754 (95% CI 0.605-0.903), respectively. The results showed a pooled PPV, and NPV based on the random effect model of 0.458 (95% CI 0.339-0.577), and 0.901 (95% CI 0.829-0.972) respectively. The pooled DCE-MRI accuracy to predict pCR to neoadjuvant chemotherapy was 0.768 (95% CI 0.720-0.817).
Conclusion: According to our results IVIM parameters and DCE-MRI is play a potential role in early prediction of response to NAC in BC. The superior sensitivity and specificity for diffusion-weighted advanced (IVIM) imaging and DCE parameter means that these approaches can be used as a suitable method in early prediction of response to breast tumors.
References
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12. Almahariq MF, Quinn TJ, Siddiqui ZA, Thompson AB, Jawad MS, Chen PY, et al. Post-mastectomy radiotherapy is associated with improved overall survival in T3N0 patients who do not receive chemotherapy. Radiotherapy and Oncology. 2020;145:229-37.
13. Cassidy MR, Zabor EC, Stempel M, Mehrara B, Gemignani ML. Does response to neo‐adjuvant chemotherapy impact breast reconstruction? The breast journal. 2018;24(4):567-73.
14. Padhani AR, Liu G, Mu-Koh D, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102-25.
15. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images. NMR in Biomedicine. 1995;8(7):333-44.
16. Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011;31(4):1059-84.
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19. Squillaci E, Manenti G, Cova M, Di Roma M, Miano R, Palmieri G, et al. Correlation of diffusion-weighted MR imaging with cellularity of renal tumours. Anticancer research. 2004;24(6):4175-80.
20. Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. American journal of Roentgenology. 2009;193(6):1716-22.
21. Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, Corona-Villalobos CP, Kamel IR, et al. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics. 2011;31(6):1773-91.
22. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401-7.
23. Koh D-M, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. American Journal of Roentgenology. 2011;196(6):1351-61.
24. Le Bihan D, Breton E, Lallemand D, Aubin M, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497-505.
25. Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, et al. Intravoxel incoherent motion diffusion‐weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve‐fitting methods. Journal of Magnetic Resonance Imaging. 2015;42(2):362-70.
26. Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magnetic resonance in medicine. 2011;65(5):1437-47.
27. Gubern-Mérida A, Martí R, Melendez J, Hauth JL, Mann RM, Karssemeijer N, et al. Automated localization of breast cancer in DCE-MRI. Medical image analysis. 2015;20(1):265-74.
28. Padhani A. Dynamic contrast-enhanced MRI studies in human tumours. The British journal of radiology. 1999;72(857):427-31.
29. Gordon Y, Partovi S, Müller-Eschner M, Amarteifio E, Bäuerle T, Weber M-A, et al. Dynamic contrast-enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion. Cardiovascular diagnosis and therapy. 2014;4(2):147.
30. Bufi E, Belli P, Costantini M, Cipriani A, Di Matteo M, Bonatesta A, et al. Role of the apparent diffusion coefficient in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Clinical Breast Cancer. 2015;15(5):370-80.
31. Li X, Wang Q, Dou Y, Zhang Y, Tao J, Yang L, et al. Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade? Skeletal Radiology. 2020;49:1829-38.
32. Zhou J, Zhang Y, Chang KT, Lee KE, Wang O, Li J, et al. Diagnosis of benign and malignant breast lesions on DCE‐MRI by using radiomics and deep learning with consideration of peritumor tissue. Journal of Magnetic Resonance Imaging. 2020;51(3):798-809.
33. Gampenrieder SP, Peer A, Weismann C, Meissnitzer M, Rinnerthaler G, Webhofer J, et al. Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR). Breast Cancer Research. 2019;21:1-11.
34. Pesapane F, Rotili A, Botta F, Raimondi S, Bianchini L, Corso F, et al. Radiomics of MRI for the prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients: a single referral centre analysis. Cancers. 2021;13(17):4271.
35. Chen X, Chen X, Yang J, Li Y, Fan W, Yang Z. 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. Journal of computer assisted tomography. 2020;44(2):275-83.
36. Dongfeng H, Daqing M, Erhu J. Dynamic breast magnetic resonance imaging: pretreatment prediction of tumor response to neoadjuvant chemotherapy. Clinical Breast Cancer. 2012;12(2):94-101.
37. Fan M, Chen H, You C, Liu L, Gu Y, Peng W, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Frontiers in Molecular Biosciences. 2021;8:622219.
38. Tudorica A, Oh KY, Chui SY, Roy N, Troxell ML, Naik A, et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. Translational oncology. 2016;9(1):8-17.
39. Zhou J, Liu Y-L, Zhang Y, Chen J-H, Combs FJ, Parajuli R, et al. BI-RADS reading of non-mass lesions on DCE-MRI and differential diagnosis performed by radiomics and deep learning. Frontiers in Oncology. 2021;11:728224.
40. Tateishi U, Miyake M, Nagaoka T, Terauchi T, Kubota K, Kinoshita T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging—prospective assessment. Radiology. 2012;263(1):53-63.
41. Tokuda Y, Yanagawa M, Fujita Y, Honma K, Tanei T, Shimoda M, et al. Prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer: comparison of diagnostic performances of dedicated breast PET, whole-body PET, anddynamic contrast-enhanced MRI. Breast Cancer Research and Treatment. 2021;188:107-15.
42. De Los Santos J, Bernreuter W, Keene K, Krontiras H, Carpenter J, Bland K, et al. Accuracy of breast magnetic resonance imaging in predicting pathologic response in patients treated with neoadjuvant chemotherapy. Clinical breast cancer. 2011;11(5):312-9.
43. Moon M, Cornfeld D, Weinreb J. Dynamic contrast-enhanced breast MR imaging. Magnetic resonance imaging clinics of North America. 2009;17(2):351-62.
44. Craciunescu OI, Blackwell KL, Jones EL, MacFall JR, Yu D, Vujaskovic Z, et al. DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study. International Journal of Hyperthermia. 2009;25(6):405-15.
45. Schott AF, Roubidoux MA, Helvie MA, Hayes DF, Kleer CG, Newman LA, et al. Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy. Breast cancer research and treatment. 2005;92:231-8.
46. Bedair R, Priest AN, Patterson AJ, McLean MA, Graves MJ, Manavaki R, et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. European radiology. 2017;27:2726-36.
47. Che S, Zhao X, Yanghan O, Li J, Wang M, Wu B, et al. Role of the intravoxel incoherent motion diffusion weighted imaging in the pre-treatment prediction and early response monitoring to neoadjuvant chemotherapy in locally advanced breast cancer. Medicine. 2016;95(4).
48. Kim Y, Kim SH, Lee HW, Song BJ, Kang BJ, Lee A, et al. Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer. Magnetic Resonance Imaging. 2018;48:27-33.
49. Cho GY, Gennaro L, Sutton EJ, Zabor EC, Zhang Z, Giri D, et al. Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients. European journal of radiology open. 2017;4:101-7.
50. Suo S, Yin Y, Geng X, Zhang D, Hua J, Cheng F, et al. Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models. Journal of Translational Medicine. 2021;19(1):1-12.
51. Wolmark N, Wang J, Mamounas E, Bryant J, Fisher B. Preoperative chemotherapy in patients with operable breast cancer: nine-year results from National Surgical Adjuvant Breast and Bowel Project B-18. JNCI Monographs. 2001;2001(30):96-102.
52. Mauri D, Pavlidis N, Ioannidis JP. Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. Journal of the National Cancer Institute. 2005;97(3):188-94.
53. Nagao T, Kinoshita T, Hojo T, Tsuda H, Tamura K, Fujiwara Y. The differences in the histological types of breast cancer and the response to neoadjuvant chemotherapy: the relationship between the outcome and the clinicopathological characteristics. The Breast. 2012;21(3):289-95.
54. Prevos R, Smidt M, Tjan-Heijnen V, van Goethem M, Beets-Tan R, Wildberger J, et al. Pre-treatment differences and early response monitoring of neoadjuvant chemotherapy in breast cancer patients using magnetic resonance imaging: a systematic review. European radiology. 2012;22:2607-16.
55. Marinovich M, Sardanelli F, Ciatto S, Mamounas E, Brennan M, Macaskill P, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. The Breast. 2012;21(5):669-77.
56. Cheng Q, Huang J, Liang J, Ma M, Ye K, Shi C, et al. The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Frontiers in Oncology. 2020;10:93.
2. Naseem U, Rashid J, Ali L, Kim J, Haq QEU, Awan MJ, et al. An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers. IEEE Access. 2022;10:78242-52.
3. Keogh L, Steel E, Weideman P, Butow P, Collins I, Emery J, et al. Consumer and clinician perspectives on personalising breast cancer prevention information. The Breast. 2019;43:39-47.
4. Jun W, Cong W, Xianxin X, Daqing J. Meta-analysis of quantitative dynamic contrast-enhanced MRI for the assessment of neoadjuvant chemotherapy in breast cancer. The American Surgeon. 2019;85(6):645-53.
5. Kim J, Oktay K, Gracia C, Lee S, Morse C, Mersereau JE. Which patients pursue fertility preservation treatments? A multicenter analysis of the predictors of fertility preservation in women with breast cancer. Fertility and sterility. 2012;97(3):671-6.
6. Pilewskie M, Zabor EC, Mamtani A, Barrio AV, Stempel M, Morrow M. The optimal treatment plan to avoid axillary lymph node dissection in early-stage breast cancer patients differs by surgical strategy and tumor subtype. Annals of surgical oncology. 2017;24:3527-33.
7. de Munck L, Sonke G, van Dalen T, van Diest P, van den Bongard H, Peeters P, et al. Population based study on sentinel node biopsy before or after neoadjuvant chemotherapy in clinically node negative breast cancer patients: Identification rate and influence on axillary treatment. European journal of cancer. 2015;51(8):915-21.
8. Bear HD, Anderson S, Brown A, Smith R, Mamounas EP, Fisher B, et al. The effect on tumor response of adding sequential preoperative docetaxel to preoperative doxorubicin and cyclophosphamide: preliminary results from National Surgical Adjuvant Breast and Bowel Project Protocol B-27. Journal of Clinical Oncology. 2003;21(22):4165-74.
9. Kaufmann M, Von Minckwitz G, Bear H, Buzdar A, McGale P, Bonnefoi H, et al. Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Annals of Oncology. 2007;18(12):1927-34.
10. Ah-See M-LW, Makris A, Taylor NJ, Harrison M, Richman PI, Burcombe RJ, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clinical Cancer Research. 2008;14(20):6580-9.
11. Abramson RG, Li X, Hoyt TL, Su P-F, Arlinghaus LR, Wilson KJ, et al. Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results. Magnetic resonance imaging. 2013;31(9):1457-64.
12. Almahariq MF, Quinn TJ, Siddiqui ZA, Thompson AB, Jawad MS, Chen PY, et al. Post-mastectomy radiotherapy is associated with improved overall survival in T3N0 patients who do not receive chemotherapy. Radiotherapy and Oncology. 2020;145:229-37.
13. Cassidy MR, Zabor EC, Stempel M, Mehrara B, Gemignani ML. Does response to neo‐adjuvant chemotherapy impact breast reconstruction? The breast journal. 2018;24(4):567-73.
14. Padhani AR, Liu G, Mu-Koh D, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102-25.
15. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images. NMR in Biomedicine. 1995;8(7):333-44.
16. Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011;31(4):1059-84.
17. Atuegwu NC, Arlinghaus LR, Li X, Welch EB, Chakravarthy BA, Gore JC, et al. Integration of diffusion‐weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy. Magnetic Resonance in Medicine. 2011;66(6):1689-96.
18. Yoshikawa MI, Ohsumi S, Sugata S, Kataoka M, Takashima S, Mochizuki T, et al. Relation between cancer cellularity and apparent diffusion coefficient values using diffusion-weighted magnetic resonance imaging in breast cancer. Radiation medicine. 2008;26:222-6.
19. Squillaci E, Manenti G, Cova M, Di Roma M, Miano R, Palmieri G, et al. Correlation of diffusion-weighted MR imaging with cellularity of renal tumours. Anticancer research. 2004;24(6):4175-80.
20. Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. American journal of Roentgenology. 2009;193(6):1716-22.
21. Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, Corona-Villalobos CP, Kamel IR, et al. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics. 2011;31(6):1773-91.
22. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401-7.
23. Koh D-M, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. American Journal of Roentgenology. 2011;196(6):1351-61.
24. Le Bihan D, Breton E, Lallemand D, Aubin M, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497-505.
25. Suo S, Lin N, Wang H, Zhang L, Wang R, Zhang S, et al. Intravoxel incoherent motion diffusion‐weighted MR imaging of breast cancer at 3.0 tesla: comparison of different curve‐fitting methods. Journal of Magnetic Resonance Imaging. 2015;42(2):362-70.
26. Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magnetic resonance in medicine. 2011;65(5):1437-47.
27. Gubern-Mérida A, Martí R, Melendez J, Hauth JL, Mann RM, Karssemeijer N, et al. Automated localization of breast cancer in DCE-MRI. Medical image analysis. 2015;20(1):265-74.
28. Padhani A. Dynamic contrast-enhanced MRI studies in human tumours. The British journal of radiology. 1999;72(857):427-31.
29. Gordon Y, Partovi S, Müller-Eschner M, Amarteifio E, Bäuerle T, Weber M-A, et al. Dynamic contrast-enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion. Cardiovascular diagnosis and therapy. 2014;4(2):147.
30. Bufi E, Belli P, Costantini M, Cipriani A, Di Matteo M, Bonatesta A, et al. Role of the apparent diffusion coefficient in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Clinical Breast Cancer. 2015;15(5):370-80.
31. Li X, Wang Q, Dou Y, Zhang Y, Tao J, Yang L, et al. Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade? Skeletal Radiology. 2020;49:1829-38.
32. Zhou J, Zhang Y, Chang KT, Lee KE, Wang O, Li J, et al. Diagnosis of benign and malignant breast lesions on DCE‐MRI by using radiomics and deep learning with consideration of peritumor tissue. Journal of Magnetic Resonance Imaging. 2020;51(3):798-809.
33. Gampenrieder SP, Peer A, Weismann C, Meissnitzer M, Rinnerthaler G, Webhofer J, et al. Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR). Breast Cancer Research. 2019;21:1-11.
34. Pesapane F, Rotili A, Botta F, Raimondi S, Bianchini L, Corso F, et al. Radiomics of MRI for the prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients: a single referral centre analysis. Cancers. 2021;13(17):4271.
35. Chen X, Chen X, Yang J, Li Y, Fan W, Yang Z. 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. Journal of computer assisted tomography. 2020;44(2):275-83.
36. Dongfeng H, Daqing M, Erhu J. Dynamic breast magnetic resonance imaging: pretreatment prediction of tumor response to neoadjuvant chemotherapy. Clinical Breast Cancer. 2012;12(2):94-101.
37. Fan M, Chen H, You C, Liu L, Gu Y, Peng W, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer. Frontiers in Molecular Biosciences. 2021;8:622219.
38. Tudorica A, Oh KY, Chui SY, Roy N, Troxell ML, Naik A, et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. Translational oncology. 2016;9(1):8-17.
39. Zhou J, Liu Y-L, Zhang Y, Chen J-H, Combs FJ, Parajuli R, et al. BI-RADS reading of non-mass lesions on DCE-MRI and differential diagnosis performed by radiomics and deep learning. Frontiers in Oncology. 2021;11:728224.
40. Tateishi U, Miyake M, Nagaoka T, Terauchi T, Kubota K, Kinoshita T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging—prospective assessment. Radiology. 2012;263(1):53-63.
41. Tokuda Y, Yanagawa M, Fujita Y, Honma K, Tanei T, Shimoda M, et al. Prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer: comparison of diagnostic performances of dedicated breast PET, whole-body PET, anddynamic contrast-enhanced MRI. Breast Cancer Research and Treatment. 2021;188:107-15.
42. De Los Santos J, Bernreuter W, Keene K, Krontiras H, Carpenter J, Bland K, et al. Accuracy of breast magnetic resonance imaging in predicting pathologic response in patients treated with neoadjuvant chemotherapy. Clinical breast cancer. 2011;11(5):312-9.
43. Moon M, Cornfeld D, Weinreb J. Dynamic contrast-enhanced breast MR imaging. Magnetic resonance imaging clinics of North America. 2009;17(2):351-62.
44. Craciunescu OI, Blackwell KL, Jones EL, MacFall JR, Yu D, Vujaskovic Z, et al. DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study. International Journal of Hyperthermia. 2009;25(6):405-15.
45. Schott AF, Roubidoux MA, Helvie MA, Hayes DF, Kleer CG, Newman LA, et al. Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy. Breast cancer research and treatment. 2005;92:231-8.
46. Bedair R, Priest AN, Patterson AJ, McLean MA, Graves MJ, Manavaki R, et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. European radiology. 2017;27:2726-36.
47. Che S, Zhao X, Yanghan O, Li J, Wang M, Wu B, et al. Role of the intravoxel incoherent motion diffusion weighted imaging in the pre-treatment prediction and early response monitoring to neoadjuvant chemotherapy in locally advanced breast cancer. Medicine. 2016;95(4).
48. Kim Y, Kim SH, Lee HW, Song BJ, Kang BJ, Lee A, et al. Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer. Magnetic Resonance Imaging. 2018;48:27-33.
49. Cho GY, Gennaro L, Sutton EJ, Zabor EC, Zhang Z, Giri D, et al. Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients. European journal of radiology open. 2017;4:101-7.
50. Suo S, Yin Y, Geng X, Zhang D, Hua J, Cheng F, et al. Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models. Journal of Translational Medicine. 2021;19(1):1-12.
51. Wolmark N, Wang J, Mamounas E, Bryant J, Fisher B. Preoperative chemotherapy in patients with operable breast cancer: nine-year results from National Surgical Adjuvant Breast and Bowel Project B-18. JNCI Monographs. 2001;2001(30):96-102.
52. Mauri D, Pavlidis N, Ioannidis JP. Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. Journal of the National Cancer Institute. 2005;97(3):188-94.
53. Nagao T, Kinoshita T, Hojo T, Tsuda H, Tamura K, Fujiwara Y. The differences in the histological types of breast cancer and the response to neoadjuvant chemotherapy: the relationship between the outcome and the clinicopathological characteristics. The Breast. 2012;21(3):289-95.
54. Prevos R, Smidt M, Tjan-Heijnen V, van Goethem M, Beets-Tan R, Wildberger J, et al. Pre-treatment differences and early response monitoring of neoadjuvant chemotherapy in breast cancer patients using magnetic resonance imaging: a systematic review. European radiology. 2012;22:2607-16.
55. Marinovich M, Sardanelli F, Ciatto S, Mamounas E, Brennan M, Macaskill P, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. The Breast. 2012;21(5):669-77.
56. Cheng Q, Huang J, Liang J, Ma M, Ye K, Shi C, et al. The diagnostic performance of DCE-MRI in evaluating the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Frontiers in Oncology. 2020;10:93.
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Jun 4, 2023
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Mohanad Ahmed Sahib, Arian Arvin, Nasrin Ahmadinejad, & Raad Ajeel Bustan. (2023). A Meta-Analysis Study Of Quantitative Intravoxel Incoherent Motion (DWI) And Dynamic Contrast-Enhanced MRI To Evaluate Neoadjuvant Chemotherapy In Breast Cancer. Journal of Population Therapeutics and Clinical Pharmacology, 30(13), 362-372. https://doi.org/10.47750/jptcp.2023.30.13.037
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Mohanad Ahmed Sahib, Arian Arvin, Nasrin Ahmadinejad, & Raad Ajeel Bustan. (2023). A Meta-Analysis Study Of Quantitative Intravoxel Incoherent Motion (DWI) And Dynamic Contrast-Enhanced MRI To Evaluate Neoadjuvant Chemotherapy In Breast Cancer. Journal of Population Therapeutics and Clinical Pharmacology, 30(13), 362-372. https://doi.org/10.47750/jptcp.2023.30.13.037