Evaluation of treatment responses among subgroups of breast cancer patients receiving neoadjuvant chemotherapy

被引:0
作者
Dagistanli, Sevinc [1 ]
Sonmez, Suleyman [2 ]
Bulut, Nilufer [3 ]
Kose, Ali Mertcan [4 ]
机构
[1] Kanuni Sultan Suleyman Res & Training Hosp, Dept Gen Surg, Istanbul, Turkiye
[2] Kanuni Sultan Suleyman Res & Training Hosp, Dept Radiol, Istanbul, Turkiye
[3] Basaksehir Cam & Sakura City Hosp, Dept Med Oncol, Istanbul, Turkiye
[4] Istanbul Ticaret Univ, Vocat Sch, Dept Comp Programming, Istanbul, Turkiye
关键词
Breast cancer; MRI; pathological response; MRI;
D O I
10.4103/jcrt.jcrt_1409_22
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Breast MRIs are helpful for determining treatment plans, responses, and prospective survival analyses. In this retrospective cross-sectional study, we compared the preoperative MRI treatment response to neoadjuvant chemotherapy (NAC) administration with the postoperative pathological response in breast cancer patients. Materials and Methods: We analyzed data from 108 hospitalized patients receiving NAC between 2020 and 2022. We used MRI to evaluate the treatment response to NAC in patients with locally advanced breast cancers who had not received any prior treatment. We recorded the longest diameter of the primary tumor and the numbers of secondary tumors and axillary lymph nodes. In addition, we examined the correlation between the MRI response rate and pathological specimen results. Results: In our subgroup analyses, we found the best pathological response in patients with luminal B (Ki-67 index >14%) breast cancer and positivity for both hormone receptor and HER-2 markers. After comparing the pathological and radiological treatment responses in tumors and lymph nodes, the sensitivities were 90.3% for the pathological assessment and 42.8% for the radiological assessment, while the accuracies were 84.2% for the pathological assessment and 61.1% for the radiological assessment. Conclusion: Using MRI techniques and sequence intervals and examining the histopathological characteristics of tumors may help increase the accuracy of the pathological complete response.
引用
收藏
页码:S821 / S826
页数:6
相关论文
共 22 条
  • [1] PET-CT and MR Imaging in the Management of Axillary Nodes in Early Stage Breast Cancer
    Baran, Mehmet Tarik
    Gundogdu, Hasan
    Demiral, Gokhan
    Kupik, Osman
    Arpa, Medeni
    Pergel, Ahmet
    [J]. JCPSP-JOURNAL OF THE COLLEGE OF PHYSICIANS AND SURGEONS PAKISTAN, 2020, 30 (09): : 946 - 950
  • [2] MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer
    Cattell, Renee F.
    Kang, James J.
    Ren, Thomas
    Huang, Pauline B.
    Muttreja, Ashima
    Dacosta, Sarah
    Li, Haifang
    Baer, Lea
    Clouston, Sean
    Palermo, Roxanne
    Fisher, Paul
    Bernstein, Cliff
    Cohen, Jules A.
    Duong, Tim Q.
    [J]. CLINICAL BREAST CANCER, 2020, 20 (01) : 68 - +
  • [3] Clinical and molecular characteristics of HER2-low-positive breast cancer: pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials
    Denkert, Carsten
    Seither, Fenja
    Schneeweiss, Andreas
    Link, Theresa
    Blohmer, Jens-Uwe
    Just, Marianne
    Wimberger, Pauline
    Forberger, Almuth
    Tesch, Hans
    Jackisch, Christian
    Schmatloch, Sabine
    Reinisch, Mattea
    Solomayer, Erich F.
    Schmitt, Wolfgang D.
    Hanusch, Claus
    Fasching, Peter A.
    Luebbe, Kristina
    Solbach, Christine
    Huober, Jens
    Rhiem, Kerstin
    Marme, Frederik
    Reimer, Toralf
    Schmidt, Marcus
    Sinn, Bruno, V
    Janni, Wolfgang
    Stickeler, Elmar
    Michel, Laura
    Stoetzer, Oliver
    Hahnen, Eric
    Furlanetto, Jenny
    Seiler, Sabine
    Nekljudova, Valentina
    Untch, Michael
    Loibl, Sibylle
    [J]. LANCET ONCOLOGY, 2021, 22 (08) : 1151 - 1161
  • [4] New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
    Eisenhauer, E. A.
    Therasse, P.
    Bogaerts, J.
    Schwartz, L. H.
    Sargent, D.
    Ford, R.
    Dancey, J.
    Arbuck, S.
    Gwyther, S.
    Mooney, M.
    Rubinstein, L.
    Shankar, L.
    Dodd, L.
    Kaplan, R.
    Lacombe, D.
    Verweij, J.
    [J]. EUROPEAN JOURNAL OF CANCER, 2009, 45 (02) : 228 - 247
  • [5] Pretreatment MRI features associated with diagnostic accuracy of post-treatment MRI after neoadjuvant chemotherapy
    Eun, N. L.
    Gweon, H. M.
    Son, E. J.
    Youk, J. H.
    Kim, J-A
    [J]. CLINICAL RADIOLOGY, 2018, 73 (07) : 676.e9 - 676.e14
  • [6] Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer
    Eun, Na Lae
    Kang, Daesung
    Son, Eun Ju
    Park, Jeong Seon
    Youk, Ji Hyun
    Kim, Jeong-Ah
    Gweon, Hye Mi
    [J]. RADIOLOGY, 2020, 294 (01) : 31 - 41
  • [7] A Clinical-Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases
    Gan, Liangyu
    Ma, Mingming
    Liu, Yinhua
    Liu, Qian
    Xin, Ling
    Cheng, Yuanjia
    Xu, Ling
    Qin, Naishan
    Jiang, Yuan
    Zhang, Xiaodong
    Wang, Xiaoying
    Ye, Jingming
    [J]. FRONTIERS IN ONCOLOGY, 2021, 11
  • [8] Diagnostic performance of standard breast MR imaging compared to dedicated axillary MR imaging in the evaluation of axillary lymph node
    Ha, Su Min
    Chae, Eun Young
    Cha, Joo Hee
    Shin, Hee Jung
    Choi, Woo Jung
    Kim, Hak Hee
    [J]. BMC MEDICAL IMAGING, 2020, 20 (01)
  • [9] Monitoring of neoadjuvant chemotherapy using multiparametric, 23Na sodium MR, and multimodality (PET/CT/MRI) imaging in locally advanced breast cancer
    Jacobs, Michael A.
    Ouwerkerk, Ronald
    Wolff, Antonio C.
    Gabrielson, Edward
    Warzecha, Hind
    Jeter, Stacie
    Bluemke, David A.
    Wahl, Richard
    Stearns, Vered
    [J]. BREAST CANCER RESEARCH AND TREATMENT, 2011, 128 (01) : 119 - 126
  • [10] Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
    Joo, Sunghoon
    Ko, Eun Sook
    Kwon, Soonhwan
    Jeon, Eunjoo
    Jung, Hyungsik
    Kim, Ji-Yeon
    Chung, Myung Jin
    Im, Young-Hyuck
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)