Hyperpolarized carbon-13 MRI for early response assessment of neoadjuvant chemotherapy in breast cancer patients.
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ABSTRACT:
SUBMITTER: Woitek R
PROVIDER: S-EPMC7612070 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.
| S-EPMC6995024 | biostudies-literature
Project description:This review provides a comprehensive assessment of the development of hyperpolarized (HP) carbon-13 metabolic MRI from the early days to the present with a focus on clinical applications. The status and upcoming challenges of translating HP carbon-13 into clinical application are reviewed, along with the complexity, technical advancements, and future directions. The road to clinical application is discussed regarding clinical needs and technological advancements, highlighting the most recent successes of metabolic imaging with hyperpolarized carbon-13 MRI. Given the current state of hyperpolarized carbon-13 MRI, the conclusion of this review is that the workflow for hyperpolarized carbon-13 MRI is the limiting factor.
| S-EPMC8067176 | biostudies-literature
Project description:PurposeTo assess the value of diffusion-weighted MRI (DW-MRI) in the non-invasive prediction of blastemal remnant after neoadjuvant chemotherapy in nephroblastoma.MethodsThis IRB-approved study included 32 pediatric patients with 35 tumors who underwent DW-MRI prior and after completion of neoadjuvant chemotherapy and subsequent surgical resection. Two blinded radiologists volumetrically assessed each tumor on pre- and post-neoadjuvant images and the parameters mean ADC, median ADC, 12.5th/25th/75th ADC percentile, skewness, and kurtosis were calculated. Blastemal remnant was determined per the pathology report. Associations between imaging features and blastemal remnant quartiles were examined using the Kruskal-Wallis test and adjusted for false discovery rate.ResultsInter-reader agreement was high for mean ADC, skewness, kurtosis, and volume (ICC: 0.76-0.998). Pre-therapeutic histogram parameters skewness and kurtosis were found to be higher in patients with a higher amount of blastemal remnant for reader 1 (overall p = 0.035) and for kurtosis in reader 2 (overall p = 0.032) with skewness not reaching the level of statistical significance (overall p = 0.055). Higher tumor volume on pre-treatment imaging was associated with a higher amount of blastemal remnant after therapy (overall p = 0.032 for both readers).ConclusionsPre-treatment skewness and kurtosis of ADC histogram analysis were significantly associated with a larger fraction of a blastemal remnant after neoadjuvant chemotherapy. These findings could be incorporated into a more personalized chemotherapeutic regime in these patients and offer prognostic information at the time of initial diagnosis.
| S-EPMC8215031 | biostudies-literature
Project description:BACKGROUND:The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). METHODS:Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1)?+?(2), Model III: group (3), Model IV: group (1)?+?(3), Model V: group (2)?+?(3), and Model VI: group (1)?+?(2)?+?(3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). RESULTS:The AUCs of the six models for the prediction of pCR were 0.816?±?0.033 (Model I), 0.823?±?0.020 (Model II), 0.888?±?0.025 (Model III), 0.876?±?0.015 (Model IV), 0.885?±?0.030 (Model V), and 0.874?±?0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. CONCLUSIONS:Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
| S-EPMC7003343 | biostudies-literature
Project description:BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSION: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.
| S-EPMC5907188 | biostudies-literature
Project description:We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC?=?0.78 (±0.03) vs 0.71 (±0.04), p?<?0.05 and RFS (C-statistic?=?0.76 (?±?0.05), vs 0.63 (?±?0.01)), p?<?0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p?>?0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p?<?0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
| S-EPMC6702160 | biostudies-literature
Project description:BACKGROUND: Changing the neoadjuvant chemotherapy regimen in insufficiently responding breast cancer is not a standard policy. We analysed a series of patients with 'luminal'-type breast cancer in whom the second half of neoadjuvant chemotherapy was selected based on the response to the first half. METHODS: Patients with oestrogen receptor-positive (ER+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer received three courses of neoadjuvant dose-dense doxorubicin and cyclophosphamide (ddAC). Three further courses of ddAC were administered in case of a 'favourable response' on the interim magnetic resonance imaging (MRI) and a switch to docetaxel and capecitabine (DC) was made in case of an 'unfavourable response', using previously published response criteria. The efficacy of this approach was evaluated by tumour size reductions on serial contrast-enhanced MRI, pathologic response and relapse-free survival. RESULTS: Two hundred and forty-six patients received three courses of ddAC. One hundred and sixty-four patients (67%) had a favourable response at the interim MRI, with a mean tumour size reduction of 31% after the first three courses and 34% after the second three courses. Patients with unfavourable responsive tumours had a mean tumour size reduction of 12% after three courses and received three courses of DC rather than ddAC. This led to a mean shrinkage of 27%. CONCLUSION: The tumour size reduction of initially less responsive tumours after treatment adaptation adds further evidence that a response-adapted strategy may enhance the efficacy of neoadjuvant chemotherapy.
| S-EPMC3859944 | biostudies-other
Project description:In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial.We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2).A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ? 50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2- and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ? 50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%.Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis.The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus with a potentially favorable long-term prognosis.
| S-EPMC4746087 | biostudies-literature
Project description:ObjectivesTo assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.MethodsThirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.ResultsAll patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.ConclusionDW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.Key points• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models.
| S-EPMC5486805 | biostudies-literature
Project description:The purpose of this study was to evaluate breast MRI radiomics in predicting, prior to any treatment, the response to neoadjuvant chemotherapy (NAC) in patients with invasive lymph node (LN)-positive breast cancer for two tasks: (1) prediction of pathologic complete response and (2) prediction of post-NAC LN status. Our study included 158 patients, with 19 showing post-NAC complete pathologic response (pathologic TNM stage T0,N0,MX) and 139 showing incomplete response. Forty-two patients were post-NAC LN-negative, and 116 were post-NAC LN-positive. We further analyzed prediction of response by hormone receptor subtype of the primary cancer (77 hormone receptor-positive, 39 HER2-enriched, 38 triple negative, and 4 cancers with unknown receptor status). Only pre-NAC MRIs underwent computer analysis, initialized by an expert breast radiologist indicating index cancers and metastatic axillary sentinel LNs on DCE-MRI images. Forty-nine computer-extracted radiomics features were obtained, both for the primary cancers and for the metastatic sentinel LNs. Since the dataset contained MRIs acquired at 1.5 T and at 3.0 T, we eliminated features affected by magnet strength using the Mann-Whitney U-test with the null-hypothesis that 1.5 T and 3.0 T samples were selected from populations having the same distribution. Bootstrapping and ROC analysis were used to assess performance of individual features in the two classification tasks. Eighteen features appeared unaffected by magnet strength. Pre-NAC tumor features generally appeared uninformative in predicting response to therapy. In contrast, some pre-NAC LN features were able to predict response: two pre-NAC LN features were able to predict pathologic complete response (area under the ROC curve (AUC) up to 0.82 [0.70; 0.88]), and another two were able to predict post-NAC LN-status (AUC up to 0.72 [0.62; 0.77]), respectively. In the analysis by a hormone receptor subtype, several potentially useful features were identified for predicting response to therapy in the hormone receptor-positive and HER2-enriched cancers.
| S-EPMC6768440 | biostudies-literature