MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.
MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.
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DOI：
10.1186/s13058-018-0950-x
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发表时间：
2018-04-18
期刊：
影响因子：
--
通讯作者：
Smidt ML
中科院分区：
文献类型：
--
作者：
Goorts B;Dreuning KMA;Houwers JB;Kooreman LFS;Boerma EG;Mann RM;Lobbes MBI;Smidt ML
主要目的是研究乳腺癌患者新辅助化疗和免疫治疗（NAC）中途基于磁共振成像（MRI）的反应模式与病理肿瘤反应之间的相关性。次要目的是比较 NAC 中途和 NAC 后测量的基于 MRI 的反应模式的预测价值，并测量观察者之间的变异性。 2012 年至 2015 年期间所有连续接受 NAC 治疗原发性浸润性乳腺癌的患者，以及在 NAC 之前、中途（和之后）接受乳腺 MRI 的患者均被纳入。所有乳腺肿瘤均由两名经验丰富的乳腺放射科医生在 MRI 上重新评估，并分为六种模式：0 型（完全放射学反应）； 1型（同心收缩）；类型 2（崩溃）；类型3（弥散增强）； 4型（病情稳定）； 5型（进行性疾病）。根据基于 MRI 的反应模式，计算显示病理完全缓解 (pCR)、肿瘤缩小 > 50% 和肿瘤直径缩小 > 50% 的肿瘤百分比。使用 Pearson 相关系数研究基于 MRI 的反应模式与病理性肿瘤减少之间的相关性，并使用 Cohen 的 Kappa 测试观察者间的一致性。包括患有 80 个肿瘤（4 个双侧）的患者（n = 76；平均年龄 53，范围 29-72 岁）。 NAC 中途的这些基于 MRI 的反应模式与病理评估中的肿瘤缩小之间存在显着相关性（读者 1 r = 0.33；p = 0.003 和读者 2 r = 0.45；p<0.001）。 NAC 中途的 0 型、1 型或 2 型模式在病理学评估中显示出最高的肿瘤减少率，分别在 90%、78% 和 65% 的病例中肿瘤减少 > 50%。在 NAC 中途 83% 的 0 型肿瘤中，病理学评估显示 pCR。 NAC 后基于 MRI 的反应模式与病理评估的肿瘤缩小率之间没有显着相关性（读者 1 r = − 0.17；p = 0.145 和读者 2 r = − 0.17；p = 0.146）。 NAC 后 41% 的 0 型肿瘤的病理学评估显示 pCR。 NAC 中途基于 MRI 的反应模式比 NAC 后基于 MRI 的反应模式可以更准确地预测病理反应。 NAC 中途的完全放射学反应与 83% 的 pCR 相关，而 NAC 后的完全放射学反应似乎仅在 41% 的病例中是正确的。本文的在线版本 (10.1186/s13058-018-0950-x) 包含补充材料，可供授权用户使用。
The main purpose was to investigate the correlation between magnetic resonance imaging (MRI)-based response patterns halfway through neoadjuvant chemotherapy and immunotherapy (NAC) and pathological tumor response in patients with breast cancer. Secondary purposes were to compare the predictive value of MRI-based response patterns measured halfway through NAC and after NAC and to measure interobserver variability. All consecutive patients treated with NAC for primary invasive breast cancer from 2012 to 2015 and who underwent breast MRI before, halfway through (and after) NAC were included. All breast tumors were reassessed on MRI by two experienced breast radiologists and classified into six patterns: type 0 (complete radiologic response); type 1 (concentric shrinkage); type 2 (crumbling); type 3 (diffuse enhancement); type 4 (stable disease); type 5 (progressive disease). Percentages of tumors showing pathological complete response (pCR), > 50% tumor reduction and > 50% tumor diameter reduction per MRI-based response pattern were calculated. Correlation between MRI-based response patterns and pathological tumor reduction was studied with Pearson’s correlation coefficient, and interobserver agreement was tested with Cohen’s Kappa. Patients (n = 76; mean age 53, range 29–72 years) with 80 tumors (4 bilateral) were included. There was significant correlation between these MRI-based response patterns halfway through NAC and tumor reduction on pathology assessment (reader 1 r = 0.33; p = 0.003 and reader 2 r = 0.45; p < 0.001). Type-0, type-1 or type-2 patterns halfway through NAC showed highest tumor reduction rates on pathology assessment, with > 50% tumor reduction in 90%, 78% and 65% of cases, respectively. In 83% of tumors with type 0 halfway through NAC, pathology assessment showed pCR. There was no significant correlation between MRI-based response patterns after NAC and tumor reduction rates on pathology assessment (reader 1 r = − 0.17; p = 0.145 and reader 2 r = − 0.17; p = 0.146). In 41% of tumors with type 0 after NAC, pathology assessment showed pCR. MRI-based response patterns halfway through NAC can predict pathologic response more accurately than MRI-based response patterns after NAC. Complete radiological response halfway NAC is associated with 83% pCR, while complete radiological response after NAC seems to be correct in only 41% of cases. The online version of this article (10.1186/s13058-018-0950-x) contains supplementary material, which is available to authorized users.
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影响因子：
3.8
作者：
Goorts B;van Nijnatten TJ;de Munck L;Moossdorff M;Heuts EM;de Boer M;Lobbes MB;Smidt ML
通讯作者：
Smidt ML
影响因子：
45.3
作者：
Liedtke, Cornelia;Mazouni, Chafika;Pusztai, Lajos
通讯作者：
Pusztai, Lajos
影响因子：
2.5
作者：
Chen, Jeon-Hor;Bahri, Shadfar;Mehta, Rita S.;Carpenter, Philip M.;Mclaren, Christine E.;Chen, Wen-Pin;Fwu, Peter T.;Hsiang, David J. B.;Lane, Karen T.;Butler, John A.;Su, Min-Ying
通讯作者：
Su, Min-Ying
影响因子：
1.9
作者：
LANDIS, JR;KOCH, GG
通讯作者：
KOCH, GG
影响因子：
19.7
作者：
Hylton, Nola M.;Blume, Jeffrey D.;Schnall, Mitchell D.
通讯作者：
Schnall, Mitchell D.