Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy

Longitudinal registration of T1-weighted breast MRI: A registration algorithm (FLIRE) and clinical application

PURPOSE

MRI is commonly used to aid breast cancer diagnosis and treatment evaluation. For patients with breast cancer, neoadjuvant chemotherapy aims to reduce the tumor size and extent of surgery necessary. The current clinical standard to measure breast tumor response on MRI uses the longest tumor diameter. Radiologists also account for other tissue properties including tumor contrast or pharmacokinetics in their assessment. Accurate longitudinal image registration of breast tissue is critical to properly compare response to treatment at different timepoints.

METHODS

In this study, a deformable Fast Longitudinal Image Registration (FLIRE) algorithm was optimized for breast tissue. FLIRE was then compared to the publicly available software packages with high accuracy (DRAMMS) and fast runtime (Elastix). Patients included in the study received longitudinal T1-weighted MRI without fat saturation at two to six timepoints as part of asymptomatic screening (n = 27) or throughout neoadjuvant chemotherapy treatment (n = 32). T1-weighted images were registered to the first timepoint with each algorithm.

RESULTS

Alignment and runtime performance were compared using two-way repeated measure ANOVAs (P < 0.05). Across all patients, Pearson's correlation coefficient across the entire image volume was slightly higher with statistical significance and had less variance for FLIRE (0.98 ± 0.01 stdev) compared to DRAMMS (0.97 ± 0.03 stdev) and Elastix (0.95 ± 0.03 stdev). Additionally, FLIRE runtime (10.0 mins) was 9.0 times faster than DRAMMS (89.6 mins) and 1.5 times faster than Elastix (14.5 mins) on a Linux workstation.

CONCLUSION

FLIRE demonstrates promise for time-sensitive clinical applications due to its accuracy, robustness across patients and timepoints, and speed.

Unique magnetic resonance spectroscopy profile of intracranial meningiomas compared to gliomas: a systematic review

BACKGROUND AND PURPOSE

The goal of this study was to systematically review the metabolic profile of meningiomas using magnetic resonance spectroscopy in comparison to gliomas, as measured by mean metabolite ratios.

METHODS

Following the PRISMA guidelines, a systematic literature review was performed using the PubMed, Ovid Embase, Web of Science, and the Cochrane databases from inception to May 2021. Studies were selected based on predetermined inclusion and exclusion criteria.

RESULTS

Eight studies were ultimately selected with 207 patients included. Fifty-nine patients were diagnosed with meningioma (age = 48.4, 66.7% female) and 148 patients diagnosed with glioma (age = 56.4, 49.2% female). Three studies reported elevated Cho/Cr in meningiomas compared to gliomas (5.71 vs. 1.46, p < 0.05, 7.02 vs. 2.62, p < 0.05, and 4.64 vs. 2.52, p = 0.001). One study reported Ala/Cr to be significantly elevated in meningiomas compared to gliomas (1.30 vs. undetectable, p < 0.001). One study reported myo-Inositol/Cr to be significantly elevated in meningiomas in comparison to gliomas (1.44 vs. 1.08, p < 0.05). One study reported Glu/Cr to be significantly elevated in meningiomas in comparison to gliomas (3.47 vs. 0.89, p = 0.002). Two studies reported Cho/NAA to be significantly elevated in meningiomas in comparison to gliomas (4.46 vs. 2.6, p = 0.004, and 5.8 vs. 2.55, p < 0.05). Two studies reported NAA/Cr was significantly elevated in gliomas compared to meningiomas (undetectable vs. 1.54, p < 0.001 and undetectable vs. 0.58, p < 0.05).

CONCLUSIONS

Significant differences in metabolite ratios between tumor types were reported in Cho/Cr, Ala/Cr, Glu/Cr, Cho/NAA, myoI/Cr and NAA/Cr between meningiomas and gliomas.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation

BACKGROUND

Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer.

PURPOSE

To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer.

STUDY TYPE

Retrospective, longitudinal.

POPULATION

A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence.

ASSESSMENT

Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed.

STATISTICAL TESTS

Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis.

RESULTS

Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067).

DATA CONCLUSION

Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Magnetic Resonance Fingerprinting: A Review of Clinical Applications

ABSTRACT

Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.

Survival After Breast-Conserving Surgery Compared with that After Mastectomy in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

BACKGROUND

Breast-conserving surgery (BCS) plus radiotherapy (BCS + RT) has been shown to improve survival compared with mastectomy in patients with early breast cancer; however, whether this superiority is maintained in breast cancer patients receiving neoadjuvant chemotherapy (NCT) is unclear. We evaluated and compared the survival outcomes after BCS + RT and mastectomy in Korean women with breast cancer treated with NCT.

METHODS

We evaluated 1641 patients who received NCT before surgery (BCS or mastectomy). We performed propensity score matching to minimize potential bias due to factors other than the surgical method and compared the 5-year, disease-free survival (DFS), distant metastasis-free survival (DMFS), and overall survival (OS) rates before and after exact matching.

RESULTS

Among the 1641 patients, 839 (51.1%) underwent BCS + RT and 802 (48.9%) underwent mastectomy. Patients who underwent mastectomy had larger tumors and more frequently had positive nodes. For BCS+RT and mastectomy, the unadjusted 5-year DFS, 5-year DMFS, and 5-year OS rates were 87.0% and 73.1%, 89.5% and 77.0%, and 91.8% and 81.0%, respectively (all p < 0.05 = 0.000). After PSM, 5-year DFS, 5-year DMFS, and 5-year OS rates for BCS + RT and mastectomy were 87.6% and 69.1%, 89.7% and 76.0%, and 89.1% and 75.7%, respectively (all p < 0.05). In both unadjusted and adjusted analyses accounting for various confounding factors, BCS + RT was significantly associated with improved DFS (p < 0.05), DMFS (p < 0.05), and OS (p < 0.05) rates compared with mastectomy.

CONCLUSIONS

BCS + RT does not impair DFS and OS in patients treated with NCT. Tumor biology and treatment response are significant prognostic indicators. Our results suggest that BCS + RT may be preferred in most breast cancer patients when both BCS and mastectomy are suitable.

Feasibility study of 2D Dixon-Magnetic Resonance Fingerprinting (MRF) of breast cancer

Purpose

Application of MRF to evaluate the feasibility of 2D Dixon blurring-corrected MRF (2DDb-cMRF) to differentiate breast cancer (BC) from normal fibroglandular tissue (FGT).

Methods

Prospective study on 14 patients with unilateral BC on 1.5 T system/axial T2w-TSE sequence, 2DDb-cMRF, B1 map, dynamic contrast-enhanced (DCE) T1-w GE-series. Mean T1 and T2 values and standard deviations were computed in the BC-/FGT-ROI on pre-/post-contrast MRF-maps and their differences were tested by two-tailed student t-test.Accuracy and repeatability of MRF were evaluated in a phantom experiment with gelatin with Primovist surrounded by fat.The T1 reduction between pre-/post-contrast MRF-maps was correlated to DCE signal enhancement in the last image post-contrast through the Pearson´s correlation coefficient (r) and for the phantom validation experiment through the Lin's concordance correlation coefficient (CCC).Visual evaluation of cancers on MRF-Maps was performed by rating each MRF-Map by 3 radiologists.

Results

T1- and T2-MRF values of BC vs. FGT were for T1 and T2 pre-contrast respectively: 1147 ± 1 ms vs. 1052 ± 9 ms (p = 0.007) and 83 ± 1 ms vs. 73 ± 1 ms (p = 0.03); post-contrast respectively: 367.3 ± 121.5 ms vs. 690.3 ± 200.3 ms (p = 0.0005) and 76.9 ± 11.5 ms vs. 69.8 ± 15.2 ms (p = 0.12). r was positive (FGT r = 0.7; BC r = 0.6). CCC was 0.999 for T1 and 0.994 for T2. In the T1- and T2-MRF-Maps before contrast respectively (7,7,8)/14 and (5,9,8)/14 cancers were visible to the readers; afterwards, (11,12,12)/14 and (5,6,11)/14.

Conclusions

MRF is promising for distinction between BC and FGT as well as for analyzing pre-/post-contrast T1 changes. However, its potential for differential diagnosis warrants further studies.

Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

Simple Summary

Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery.

Abstract

Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Prediction of pathologic complete response on MRI in patients with breast cancer receiving neoadjuvant chemotherapy according to molecular subtypes

OBJECTIVES

This study aimed to investigate the predictability of breast MRI for pathologic complete response (pCR) by molecular subtype in patients with breast cancer receiving neoadjuvant chemotherapy (NAC) and investigate the MRI findings that can mimic residual malignancy.

METHODS

A total of 506 patients with breast cancer who underwent MRI after NAC and underwent surgery between January and December 2018 were included. Two breast radiologists dichotomized the post-NAC MRI findings as radiologic complete response (rCR) and no-rCR. The diagnostic performance of MRI predicting pCR was evaluated. pCR was determined based on the final pathology reports. Tumors were divided according to hormone receptor (HR) and human epidermal growth factor receptor (HER) 2. Residual lesions on post-NAC MRI were divided into overt and subtle which classified as nodularity or delayed enhancement. Pearson's χ² and Wilcoxon rank-sum tests were used for MRI findings causing false-negative pCR.

RESULTS

The overall pCR rate was 30.04%. The overall accuracy for predicting pCR using MRI was 76.68%. The accuracy was significantly different by subtypes (p < 0.001), as follows in descending order: HR - /HER2 - (85.63%), HR + /HER2 - (82.84%), HR + /HER2 + (69.37%), and HR - /HER2 + (62.38%). MRI in the HR - /HER2 + type showed the highest false-negative rate (18.81%) for predicting pCR. The subtle residual enhancement observed only in the delayed phase was associated with false-negative findings (76.2%, p = 0.016).

CONCLUSIONS

The diagnostic accuracy of MRI for predicting pCR differed by molecular subtypes. When the residual enhancement on MRI after NAC is subtle and seen only in the delayed phase, overinterpretation of residual tumors should be performed with caution.

KEY POINTS

• In patients with breast cancer after completion of neoadjuvant chemotherapy, the diagnostic accuracy of MRI for predicting pathologic complete response (pCR) differed according to molecular subtype. • When residual enhancement on MRI is subtle and seen only in the delayed phase, this finding could be associated with false-negative pCR results.

Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

BACKGROUND

Radiomics involving quantitative analysis of imaging has shown promises in oncology to serve as non-invasive biomarkers. We investigated whether pre-treatment T2-weighted magnetic resonance imaging (MRI) can be used to predict response to neoadjuvant chemotherapy (NAC) in breast cancer.

MATERIALS AND METHODS

MRI scans were obtained for 102 patients with locally advanced breast cancer (LABC). All patients were treated with standard regimens of NAC as decided by the treating oncologist, followed by surgery and adjuvant treatment according to standard institutional practice. The primary tumor was segmented, and 11 texture features were extracted using the grey-level co-occurrence matrices analysis of the T2W-images from tumor cores and margins. Response assessment was done using clinical-pathological responses with patients classified into binary groups: responders and non-responders. Machine learning classifiers were used to develop a radiomics model, and a leave-one-out cross-validation technique was used to assess the performance.

RESULTS

7 features were significantly (p < 0.05) different between the two response groups. The best classification accuracy was obtained using a k-nearest neighbor (kNN) model with sensitivity, specificity, accuracy, and area under curve of 63, 93, 87, and 0.78, respectively.

CONCLUSIONS

Pre-treatment T2-weighted MRI texture features can predict NAC response with reasonable accuracy.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.

Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.

Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).

Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects

PURPOSE

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T₁ and T₂ estimation in the female breast at 1.5T.

METHODS

Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.

RESULTS

Theoretic analysis predicts T₁ under-/T₂ overestimation if the nominal flip angles are underestimated and inversely, T₁ under-/T₂ overestimation if omitting slice profile correction, and T₁ under-/T₂ underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T₂ left-right inhomogeneity between both breasts. Their correction decreased the T₂ difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T₂ most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.

CONCLUSION

Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

Analysis of the serial circulating tumor cell count during neoadjuvant chemotherapy in breast cancer patients

We evaluated the prognostic implications of the circulating tumor cell (CTC) count in non-metastatic, HER2-negative breast cancer patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy (NCT). A total of 173, non-metastatic breast cancer patients treated with NCT were prospectively enrolled. CTCs were obtained from blood drawn pre-NCT and post-NCT using a SMART BIOPSY SYSTEM isolation kit (Cytogen Inc., Seoul, Korea) with immunofluorescence staining. Excluding 26 HER2-positive patients, Relapse-free survival (RFS) and overall survival (OS) related to the CTC count and the association of the CTC count with the treatment response to given therapy were analyzed in 147 HER2-negative patients. Among 147 HER2-negative patients, 28 relapses (19.0%) and 13 deaths (8.8%, all breast cancer-specific) were observed during a median follow-up of 37.3 months. One hundred and seven patients (72.8%) were hormone receptor-positive, and 40 patients (27.2%) had triple-negative breast cancer (TNBC). One or more CTCs were identified in 88 of the 147 patients (59.9%) before NCT and 77 of the 134 patients (52.4%) after NCT. In the entire HER2-negative patient cohort, the initial nodal status was the most significant factor influencing RFS and OS. In TNBC, 11 patients (27.5%) achieved pCR and patients that failed to achieve pCR with ≥ 5 CTCs after NCT, showed worse RFS (HR, 10.66; 95% CI, 1.80–63.07; p = 0.009) and OS (HR, 14.00; 95% CI, 1.26–155.53; p = 0.032). The patients with residual tumor and a high number of the CTCs after NCT displayed the worse outcome. These findings could provide justification to launch a future, well designed trial with longer follow-up data to obtain regulatory approval for clinical use of the assay, especially for the ER-positive, HER2-negative breast cancer subset.

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient's tumor on multiparametric MRI is insufficient to predict that patient's response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation.

Focus on the glycerophosphocholine pathway in choline phospholipid metabolism of cancer

Activated choline metabolism is a hallmark of carcinogenesis and tumor progression, which leads to elevated levels of phosphocholine and glycerophosphocholine in all types of cancer tested so far. Magnetic resonance spectroscopy applications have played a key role in detecting these elevated choline phospholipid metabolites. To date, the majority of cancer-related studies have focused on phosphocholine and the Kennedy pathway, which constitutes the biosynthesis pathway for membrane phosphatidylcholine. Fewer and more recent studies have reported on the importance of glycerophosphocholine in cancer. In this review article, we summarize the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.

Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy

OBJECTIVES

To evaluate whether multiparametric breast-MRI, obtained before the initiation of neoadjuvant systemic therapy (NST) for operable breast cancer, predicts which cancer will achieve a pathological complete response (pCR) after the completion of NST.

METHODS

This was an IRB-approved retrospective study on 31 consecutive patients (median age, 56 years) with operable invasive breast cancer (median size: 22 mm; triple-negative: 11/31 [35%], HER2-positive: 7/31 [23%], triple-positive: 13/31 [42%]) who underwent multiparametric DCE-MRI before the initiation of NST. The MRI protocol consisted of high-resolution dynamic contrast-enhanced MRI (DCE-MRI), T2-TSE, and DWI (b-values 0, 100, 800 s/mm²). The results of surgical pathology after the completion of NST served as a standard of reference. Patient characteristics (age and menopausal status), pathological tumor characteristics (type, stage, nuclear grade, ER/PR and HER2 receptor status, and Ki-67 staining), and MRI characteristics (size, morphology, T2 signal intensity, enhancement kinetics, and ADC values) before NST were evaluated and compared between patients achieving pCR vs. non-pCR.

RESULTS

Among 31 patients, 17 achieved pCR (55%) and 14 non-pCR (45%). No correlation was observed between patient- or tumor pathology-derived characteristics and pCR vs. non-pCR. Among MRI-derived tumor characteristics, tumor growth orientation parallel to Cooper's ligaments (p = 0.002) and wash-out rates (p = 0.019) correlated with pCR. Pre-NST ADC values were lower in patients achieving pCR (P = 0.086).

CONCLUSIONS

A tumor growth pattern parallel with Cooper's ligaments and a fast wash-out rate on pre-treatment multiparametric MRI are predictive of pCR and more closely associated with pCR than ADC values.

In vivo MR spectroscopy for breast cancer diagnosis

Breast cancer is a significant health concern in females, worldwide. In vivo proton (1H) MR spectroscopy (MRS) has evolved as a non-invasive tool for diagnosis and for biochemical characterization of breast cancer. Water-to-fat ratio, fat and water fractions and choline containing compounds (tCho) have been identified as diagnostic biomarkers of malignancy. Detection of tCho in normal breast tissue of volunteers and in lactating females limits the use of tCho as a diagnostic marker. Technological developments like high-field scanners, multi channel coils, pulse sequences with water and fat suppression facilitated easy detection of tCho. Also, quantification of tCho and its cut-off for objective assessment of malignancy have been reported. Meta-analysis of in vivo 1H MRS studies have documented the pooled sensitivities and the specificities in the range of 71-74% and 78-88%, respectively. Inclusion of MRS has been shown to enhance the diagnostic specificity of MRI, however, detection of tCho in small sized lesions (≤1 cm) is challenging even at high magnetic fields. Potential of MRS in monitoring the effect of chemotherapy in breast cancer has also been reported. This review briefly presents the potential clinical role of in vivo 1H MRS in the diagnosis of breast cancer, its current status and future developments.

Repeatability and reproducibility of 3D MR fingerprinting relaxometry measurements in normal breast tissue

BACKGROUND

The 3D breast magnetic resonance fingerprinting (MRF) technique enables T₁ and T₂ mapping in breast tissues. Combined repeatability and reproducibility studies on breast T₁ and T₂ relaxometry are lacking.

PURPOSE

To assess test-retest and two-visit repeatability and interscanner reproducibility of the 3D breast MRF technique in a single-institution setting.

STUDY TYPE

Prospective.

SUBJECTS

Eighteen women (median age 29 years, range, 22-33 years) underwent Visit 1 scans on scanner 1. Ten of these women underwent test-retest scan repositioning after a 10-minute interval. Thirteen women had Visit 2 scans within 7-15 days in same menstrual cycle. The remaining five women had Visit 2 scans in the same menstrual phase in next menstrual cycle. Five women were also scanned on scanner 2 at both visits for interscanner reproducibility.

FIELD STRENGTH/SEQUENCE

Two 3T MR scanners with the 3D breast MRF technique.

ASSESSMENT

T₁ and T₂ MRF maps of both breasts.

STATISTICAL TESTS

Mean T₁ and T₂ values for normal fibroglandular tissues were quantified at all scans. For variability, between and within-subjects coefficients of variation (bCV and wCV, respectively) were assessed. Repeatability was assessed with Bland-Altman analysis and coefficient of repeatability (CR). Reproducibility was assessed with interscanner coefficient of variation (CoV) and Wilcoxon signed-rank test.

RESULTS

The bCV at test-retest scans was 9-12% for T₁ , 7-17% for T₂ , wCV was <4% for T₁ , and <7% for T₂ . For two visits in same menstrual cycle, bCV was 10-15% for T₁ , 13-17% for T₂ , wCV was <7% for T₁ and <5% for T₂ . For two visits in the same menstrual phase, bCV was 6-14% for T₁ , 15-18% for T₂ , wCV was <7% for T₁ , and <9% for T₂ . For test-retest scans, CR for T₁ and T₂ were 130 msec and 11 msec. For two visit scans, CR was <290 msec for T₁ and 10-14 msec for T₂ . Interscanner CoV was 3.3-3.6% for T₁ and 5.1-6.6% for T₂ , with no differences between interscanner measurements (P = 1.00 for T₁ , P = 0.344 for T₂ ).

DATA CONCLUSION

3D breast MRF measurements are repeatable across scan timings and scanners and may be useful in clinical applications in breast imaging.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1133-1143.

Monitoring of tumor burden in vivo by optical imaging in a xenograft SCID mouse model: evaluation of two fluorescent proteins of the GFP-superfamily

BACKGROUND

Mouse models of human-malignant-melanoma (MM) are important tools to study tumor dynamics. The enhanced green fluorescent protein (EGFP) is widely used in molecular imaging approaches, together with optical scanners, and fluorescence imaging.

PURPOSE

Currently, there are no data available as to whether other fluorescent proteins are more suitable. The goal of this preclinical study was to analyze two fluorescent proteins of the GFP superfamily under real-time in vivo conditions using fluorescence reflectance imaging (FRI).

MATERIAL AND METHODS

The human melanoma cell line MeWo was stable transfected with one plasmid: pEGFP-C1 or pDsRed1-N1. We investigated two severe combined immunodeficiency (SCID)-mice groups: A (solid xenografts) and B (xenografts as metastases). After three weeks, the animals were weekly imaged by FRI. Afterwards the mice were euthanized and metastases were imaged in situ: to quantify the cutis-dependent reduction of emitted light, we compared signal intensities obtained by metastases in vivo with signal intensities obtained by in situ liver parenchyma preparations.

RESULTS

More than 90% of cells were stable transfected. EGFP-/DsRed-xenograft tumors had identical growth kinetics. In vivo the emitted light by DsRed tumors/metastases was much brighter than by EGFP. DsRed metastases were earlier (3 vs. 5 weeks) and much more sensitive detectable than EGFP metastases. Cutis-dependent reduction of emitted light was greater in EGFP than in DsRed mice (tenfold). Autofluorescence of DsRed was lower than of EGFP.

CONCLUSION

We established an in vivo xenograft mouse model (DsRed-MeWo) that is reliable, reproducible, and superior to the EGFP model as a preclinical tool to study innovative therapies by FRI under real-time in vivo conditions.

Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR

Magnetic Resonance Imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for quantitative and functional evaluation, MRI is typically used qualitatively, in which the underlying tissue properties are not measured, and thus the brightness of each pixel is not quantitatively meaningful. Positron Emission Tomography (PET) is an inherently quantitative imaging modality that interrogates functional activity within a tissue, probed by a molecule of interest coupled with an appropriate tracer. These modalities can complement one another to provide clinical information regarding both structure and function, but there are still technical and practical hurdles in the way of the integrated use of both modalities. Recent advances in MRI have moved the field in an increasingly quantitative direction, which is complementary to PET, and could also potentially help solve some of the challenges in PET/MR. Magnetic Resonance Fingerprinting (MRF) is a recently described MRI-based technique which can efficiently and simultaneously quantitatively map several tissue properties in a single exam. Here, the basic principles behind the quantitative approach of MRF are laid out, and the potential implications for combined PET/MR are discussed.

Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters

PURPOSE

The aim of this study was to develop a combined statistical model using both clinicopathological factors and texture parameters from F-FDG PET/CT to predict responses to neoadjuvant chemotherapy in patients with breast cancer.

MATERIALS AND METHODS

A total of 435 patients with breast cancer were retrospectively enrolled. Clinical and pathological data were obtained from electronic medical records. Texture parameters were extracted from pretreatment FDG PET/CT images. The end point was pathological complete response, defined as the absence of residual disease or the presence of residual ductal carcinoma in situ without residual lymph node metastasis. Multivariable logistic regression modeling was performed using clinicopathological factors and texture parameters as covariates.

RESULTS

In the multivariable logistic regression model, various factors and parameters, including HER2, histological grade or Ki-67, gradient skewness, gradient kurtosis, contrast, difference variance, angular second moment, and inverse difference moment, were selected as significant prognostic variables. The predictive power of the multivariable logistic regression model incorporating both clinicopathological factors and texture parameters was significantly higher than that of a model with only clinicopathological factors (P = 0.0067). In subgroup analysis, texture parameters, including gradient skewness and gradient kurtosis, were selected as independent prognostic factors in the HER2-negative group.

CONCLUSIONS

A combined statistical model was successfully generated using both clinicopathological factors and texture parameters to predict the response to neoadjuvant chemotherapy. Results suggest that addition of texture parameters from FDG PET/CT can provide more information regarding treatment response prediction compared with clinicopathological factors alone.

Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial

Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.

Three-dimensional MR Fingerprinting for Quantitative Breast Imaging

Purpose To develop a fast three-dimensional method for simultaneous T1 and T2 quantification for breast imaging by using MR fingerprinting. Materials and Methods In this prospective study, variable flip angles and magnetization preparation modules were applied to acquire MR fingerprinting data for each partition of a three-dimensional data set. A fast postprocessing method was implemented by using singular value decomposition. The proposed technique was first validated in phantoms and then applied to 15 healthy female participants (mean age, 24.2 years ± 5.1 [standard deviation]; range, 18-35 years) and 14 female participants with breast cancer (mean age, 55.4 years ± 8.8; range, 39-66 years) between March 2016 and April 2018. The sensitivity of the method to B₁ field inhomogeneity was also evaluated by using the Bloch-Siegert method. Results Phantom results showed that accurate and volumetric T1 and T2 quantification was achieved by using the proposed technique. The acquisition time for three-dimensional quantitative maps with a spatial resolution of 1.6 × 1.6 × 3 mm³ was approximately 6 minutes. For healthy participants, averaged T1 and T2 relaxation times for fibroglandular tissues at 3.0 T were 1256 msec ± 171 and 46 msec ± 7, respectively. Compared with normal breast tissues, higher T2 relaxation time (68 msec ± 13) was observed in invasive ductal carcinoma (P < .001), whereas no statistical difference was found in T1 relaxation time (1183 msec ± 256; P = .37). Conclusion A method was developed for breast imaging by using the MR fingerprinting technique, which allows simultaneous and volumetric quantification of T1 and T2 relaxation times for breast tissues. © RSNA, 2018 Online supplemental material is available for this article.

Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial

BACKGROUND

Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker.

PURPOSE

To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures.

STUDY TYPE

Prospective.

SUBJECTS

In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer.

FIELD STRENGTH/SEQUENCE

DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T.

ASSESSMENT

A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient.

STATISTICAL TESTS

Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients.

RESULTS

In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10^-3 mm² /sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96).

DATA CONCLUSION

Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients?

The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.

MRI for the Staging and Evaluation of Response to Therapy in Breast Cancer

Breast magnetic resonance imaging (MRI) is the most sensitive of the available imaging modalities to characterize breast cancer. Breast MRI has gained clinical acceptance for screening high-risk patients, but its role in the preoperative imaging of breast cancer patients remains controversial. This review focuses on the current indications for staging breast MRI, the evidence for and against the role of breast MRI in the preoperative staging workup, and the evaluation of treatment response of breast cancer patients.

Study of lipid metabolism by estimating the fat fraction in different breast tissues and in various breast tumor sub-types by in vivo 1H MR spectroscopy

PURPOSE

To evaluate the utility of fat fraction (FF) for the differentiation of different breast tissues and in various breast tumor subtypes using in vivo proton (¹H) magnetic resonance spectroscopy (MRS).

METHODS

¹H MRS was performed on 68 malignant, 35 benign, and 30 healthy volunteers at 1.5 T. Malignant breast tissues of patients were characterized into different subtypes based on the differences in the expression of hormone receptors and the FF was calculated. Further, the sensitivity and specificity of FF to differentiate malignant from benign and from normal breast tissues of healthy volunteers was determined using receiver operator curve (ROC) analysis.

RESULTS

A significantly lower FF of malignant (median 0.12; range 0.01-0.70) compared to benign lesions (median 0.28; range 0.02-0.71) and normal breast tissue of healthy volunteers (median 0.39; range 0.06-0.76) was observed. No significant difference in FF was seen between benign lesions and normal breast tissues of healthy volunteers. Sensitivity and specificity of 75% and 68.6%, respectively was obtained to differentiate malignant from benign lesions. For the differentiation of malignant from healthy breast tissues, 76% sensitivity and 74.5% specificity was achieved. Higher FF was seen in patients with ER-/PR- status as compared to ER+/PR+ patients. Similarly, FF of HER2neu+ tumors were significantly higher than in HER2neu- breast tumors.

CONCLUSION

The results showed the potential of in vivo ¹H MRS in providing insight into the changes in the fat content of different types of breast tissues and in various breast tumor subtypes.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI (p<0.001). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Diffusion-weighted MR imaging in prediction of response to neoadjuvant chemotherapy in patients with breast cancer

This study aims to evaluate the potential of apparent diffusion coefficient (ADC) derived from diffusion-weighted MR imaging for predicting the treatment response to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Magnetic resonance imaging was performed prior to NACT and after two cycles of NACT. The correlation between mean ADC_pre values, mean ADC_post values, changes in ADC values and changes in tumor diameters after NACT was examined using Spearman rank correlation. A total of 164 breast cancers were enrolled in this study. Mean ADC_pre values of responders ([0.85 ± 0.16] × 10^-3 mm²/s) and non-responders ([0.84 ± 0.21] × 10^-3 mm²/s) had no significant difference (P = 0.759). While mean ADC_post value of responders was significantly higher than that of non-responders ([1.17 ± 0.37] × 10^-3 mm²/s vs. [1.01 ± 0.28] × 10^-3 mm²/s; P = 0.002). Both mean ADC_post values (r = 0.288, P = 0.000) and changes in mean ADC values (r = 0.222, P = 0.004) were positively correlated to changes in tumor diameter after NACT, except for mean ADC_pre values (r = 0.031, P = 0.695). Our results indicated that mean ADC_post values and changes in ADC values after NACT might be a biological marker for assessing the efficacy of chemotherapy.

Pre-operative assessment of residual disease in locally advanced breast cancer patients: A sequential study by quantitative diffusion weighted MRI as a function of therapy

PURPOSE

The potential of diffusion weighted imaging (DWI) in assessing pathologic response and surgical margins in locally advanced breast cancer patients (n=38) undergoing neoadjuvant chemotherapy was investigated.

METHODS

DWI was performed at pre-therapy (Tp0), after I (Tp1) and III (Tp3) NACT at 1.5T. Apparent diffusion coefficient (ADC) of whole tumor (ADC_WT), solid tumor (ADC_ST), intra-tumoral necrosis (ADC_Nec) was determined. Further, ADC of 6 consecutive shells (5mm thickness each) including tumor margin to outside tumor margins (OM1 to OM5) was calculated and the data analyzed to define surgical margins.

RESULTS

Of 38 patients, 6 were pathological complete responders (pCR), 19 partial responders (pPR) and 13 were non-responders (pNR). Significant increase was observed in ADC_ST and ADC_WT in pCR and pPR following therapy. Pre-therapy ADC was significantly lower in pCR compared to pPR and pNR indicating the heterogeneous nature of tumor which may affect drug perfusion and consequently the response. ADC of outside margins (OM1, OM2, and OM3) was significantly different among pCR, pPR and pNR at Tp3 which may serve as response predictive parameter. Further, at Tp3, ADC of outside margins (OM1, OM2, and OM3) was significantly lower compared to that seen at Tp0 in pCR, indicating the presence of residual disease in these shells.

CONCLUSION

Pre-surgery information may serve as a guide to define cancer free margins and the extent of residual disease which may be useful in planning breast conservation surgery.

Magnetic resonance imaging patterns of tumor regression in breast cancer patients after neo-adjuvant chemotherapy, and an analysis of the influencing factors

The objective of this study was to analyze the patterns of breast tumor shrinkage in patients after neo-adjuvant chemotherapy (NAC) based on magnetic resonance imaging (MRI), and to evaluate the influential factors. Preoperative breast dynamic contrast-enhanced MRI was performed on 88 patients before NAC, every 2 weeks during their chemotherapy treatment, and the week before their surgery. The MRI enhancement pattern of the primary tumors was classified into one of four categories based on BI-RADS-MRI: type I (postcontrast mass image), II (multiple small masses image), III (postcontrast mass image with peripheral non-mass enhancement image), and IV (nonmass enhancement image). Multivariate regression and χ² test analyses were employed to establish significant associations. Two kinds of tumor regression patterns were observed: concentric shrinkage was observed in 39 lesions of 88 patients (44.3%), and nests or dendritic shrinkage was observed for the other 49 lesions (55.7%). ER+/HER2-, HER2+, and type I lesions were observed in 23 (62.2%), 21 (63.6%), and 29 (60.0%) patients, respectively, out of 49 nest or dendritic shrinkage pattern lesions. Triple negative breast cancer lesions, and type II, III, and IV lesions were observed in 13 (72.2%), 9 (81.8%), 10 (62.5%), and 10 (76.9%) patients, respectively, out of 39 lesions with a concentric shrinkage pattern. Molecular subtypes (χ² =7.171, P<.05) and the MRI schedule of enhancement (χ² =11.244, P<.05) were significantly associated with the tumor regression patterns. Multivariate analysis showed molecular subtypes (P<.05) and MRI pattern enhancement (P<.05) were significant predictive factors. Molecular subtypes and the MRI enhancement patterns of the primary tumors were significant predictive factors for tumor regression patterns of breast cancer after NAC.

Diffusion MRI in early cancer therapeutic response assessment

Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. Copyright © 2016 John Wiley & Sons, Ltd.

Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study

Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy- and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635–1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient’s anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation.

Targeting Phospholipid Metabolism in Cancer

All cancers tested so far display abnormal choline and ethanolamine phospholipid metabolism, which has been detected with numerous magnetic resonance spectroscopy (MRS) approaches in cells, animal models of cancer, as well as the tumors of cancer patients. Since the discovery of this metabolic hallmark of cancer, many studies have been performed to elucidate the molecular origins of deregulated choline metabolism, to identify targets for cancer treatment, and to develop MRS approaches that detect choline and ethanolamine compounds for clinical use in diagnosis and treatment monitoring. Several enzymes in choline, and recently also ethanolamine, phospholipid metabolism have been identified, and their evaluation has shown that they are involved in carcinogenesis and tumor progression. Several already established enzymes as well as a number of emerging enzymes in phospholipid metabolism can be used as treatment targets for anticancer therapy, either alone or in combination with other chemotherapeutic approaches. This review summarizes the current knowledge of established and relatively novel targets in phospholipid metabolism of cancer, covering choline kinase α, phosphatidylcholine-specific phospholipase D1, phosphatidylcholine-specific phospholipase C, sphingomyelinases, choline transporters, glycerophosphodiesterases, phosphatidylethanolamine N-methyltransferase, and ethanolamine kinase. These enzymes are discussed in terms of their roles in oncogenic transformation, tumor progression, and crucial cancer cell properties such as fast proliferation, migration, and invasion. Their potential as treatment targets are evaluated based on the current literature.

Clinical Breast MR Using MRS or DWI: Who Is the Winner?

Magnetic resonance imaging (MRI) of the breast gained a role in clinical practice thanks to the optimal sensitivity of contrast-enhanced (CE) protocols. This approach, first proposed 30 years ago and further developed as bilateral highly spatially resolved dynamic study, is currently considered superior for cancer detection to any other technique. However, other directions than CE imaging have been explored. Apart from morphologic features on unenhanced T2-weighted images, two different non-contrast molecular approaches were mainly run in vivo: proton MR spectroscopy (1H-MRS) and diffusion-weighted imaging (DWI). Both approaches have shown aspects of breast cancer (BC) hidden to CE-MRI: 1H-MRS allowed for evaluating the total choline peak (tCho) as a biomarker of malignancy; DWI showed that restricted diffusivity is correlated with high cellularity and tumor aggressiveness. Secondary evidence on the two approaches is now available from systematic reviews and meta-analyses, mainly considered in this article: pooled sensitivity ranged 71–74% for 1H-MRS and 84–91% for DWI; specificity 78–88% and 75–84%, respectively. Interesting research perspectives are opened for both techniques, including multivoxel MRS and statistical strategies for classification of MR spectra as well as diffusion tensor imaging and intravoxel incoherent motion for DWI. However, when looking at a clinical perspective, while MRS remained a research tool with important limitations, such as relatively long acquisition times, frequent low quality spectra, difficult standardization, and quantification of tCho tissue concentration, DWI has been integrated in the standard clinical protocols of breast MRI and several studies showed its potential value as a stand-alone approach for BC detection.

Effect of Radiofrequency Transmit Field Correction on Quantitative Dynamic Contrast-enhanced MR Imaging of the Breast at 3.0 T

PURPOSE

To investigate the effects of radiofrequency transmit field (B1(+)) correction on (a) the measured T1 relaxation times of normal breast tissue and malignant lesions and (b) the pharmacokinetically derived parameters of malignant breast lesions at 3 T.

MATERIALS AND METHODS

Ethics approval and informed consent were obtained. Between May 2013 and January 2014, 30 women (median age, 58 years; range, 32-83 years) with invasive ductal carcinoma of at least 10 mm were recruited to undergo dynamic contrast material-enhanced magnetic resonance (MR) imaging before surgery. B1(+) and T1 mapping sequences were performed to determine the effect of B1(+) correction on the native tissue relaxation time (T10) of fat, parenchyma, and malignant lesions in both breasts. Pharmacokinetic parameters were calculated before and after correction for B1(+) variations. Results were correlated with histologic grade by using the Kruskal-Wallis test.

RESULTS

Measurements showed a mean 37% flip angle difference between the right and left breast, which resulted in a 61% T10 difference in fat and a 41.5% difference in parenchyma between the two breasts. The T1 of lesions in the right breast increased by 58%, whereas that of lesions in the left breast decreased by 30% after B1(+) correction. The whole-tumor transendothelial permeability across the vascular compartment(K(trans)) of lesions in the right breast decreased by 41%, and that of lesions in the left breast increased by 46% after correction. A systematic increase in K(trans) was observed, with significant differences found across the histologic grades (P < .001). The effect size of B1(+) correction on K(trans) calculation was large for lesions in the right breast and moderate for lesions in the left breast (Cohen effect size, d = 0.86 and d = 0.59, respectively).

CONCLUSION

B1(+) correction demonstrates a substantial effect on the results of quantitative dynamic contrast-enhanced analysis of breast tissue at 3 T, which propagates into the pharmacokinetic analysis of tumors that is dependent on whether the tumor is located in the right or left breast.

Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment

Breast cancer is the second leading cause of cancer death among US women, and the chance of a woman developing breast cancer sometime during her lifetime is one in eight. Early detection and diagnosis to allow appropriate locoregional and systemic treatment are key to improve the odds of surviving its diagnosis. Emerging data also suggest that different breast cancer subtypes (phenotypes) may respond differently to available adjuvant therapies. There is a growing understanding that not all patients benefit equally from systemic therapies, and therapeutic approaches are being increasingly personalized based on predictive biomarkers of clinical benefit. Optimal use of established and novel radiological imaging methods, such as magnetic resonance imaging and positron emission tomography, which have different biophysical mechanisms can simultaneously identify key functional parameters. These methods provide unique multiparametric radiological signatures of breast cancer, that will improve the accuracy of early diagnosis, help select appropriate therapies for early stage disease, and allow early assessment of therapeutic benefit.

Magnetic Resonance Imaging, Digital Mammography, and Sonography: Tumor Characteristics and Tumor Biology in Primary Setting

The use of imaging in the arena of primary treatment for breast cancer is gaining importance as a technique for assessing response to chemotherapy as well as assessing the underlying tumor biology. Both mammography and ultrasound have traditionally been used, in addition to clinical evaluation, to evaluate response to treatment although they have shed little light on the underlying biological processes. Functional magnetic resonance imaging techniques have the ability to assess response to treatments in addition to providing valuable information on changes in tumor perfusion, vascular permeability, oxygenation, cellularity, proliferation, and metabolism both at baseline and after treatment. This noninvasive method of evaluating cellular function is of importance both as endpoints for clinical trials and to our understanding of the biological mechanisms of cancer.

Utilisation of MR spectroscopy and diffusion weighted imaging in predicting and monitoring of breast cancer response to chemotherapy

Neoadjuvant chemotherapy (NACT) is the standard treatment option for breast cancer as more data shows that pathologic complete response (pCR) after NACT correlates with improved prognosis. MRI is accepted as the best imaging modality for evaluating the response to NACT in many studies as compared with clinical examination and other imaging modalities. In vivo magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) studies have both emerged as potential tools to provide early response indicators based on the changes in the metabolites and the apparent diffusion coefficient (ADC) respectively. In this review article, we aim to discuss the strength and limitations of MRS and DWI in monitoring of early response breast cancer to NACT.