The Diagnostic Performance of DCE-MRI in Evaluating the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization

BACKGROUND AND PURPOSE

The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non-invasive system for identifying HER2 and HR in breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

In light of the absence of high-performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY 1) and I-SPY 2, were incorporated. I-SPY 2 was utilized for model training and validation, while I-SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction.

RESULTS

The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I-SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I-SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction.

CONCLUSION

This study proposes a non-invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre-trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic.

The contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Ultrasound-based radiomics for early predicting response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review with meta-analysis

OBJECTIVE

This study aims to evaluate the diagnostic accuracy of ultrasound imaging (US)-based radiomics for the early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

We comprehensively searched PubMed, Cochrane Library, Embase, and Web of Science databases up to 1 January 2023 for eligible studies. We assessed the methodological quality of the enrolled studies with Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. We performed meta-analyses to summarize the diagnostic efficacy of US-based radiomics in response to NAC in breast cancer patients.

RESULTS

Eight studies proved eligible. Eligible studies exhibited an average RQS score of 12.88 (35.8% of the total score), with the RQS score ranging from 8 to 19. In the meta-analyses, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.87 (95% CI 0.81-0.92), 0.78 (95% CI 0.72-0.83), 4.02 (95% CI 3.18-5.08), 0.16 (95% CI 0.10-0.25), and 25.17 (95% CI 15.10-41.95), respectively. Results from subgroup analyses indicated that prospective studies apparently exhibited more optimal sensitivity than retrospective studies. Sensitivity analyses exhibited similar results to the primary analyses.

CONCLUSION

US-based radiomics may be a potentially crucial adjuvant method for evaluating the response of breast cancer to NAC. Due to limited data available and low quality of eligible studies, more multicenter prospective studies with rigorous methods are required to confirm our findings.

Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models

PURPOSE

Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.

MATERIALS AND METHODS

A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.

CONCLUSIONS

Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.

Dynamic contrast-enhanced magnetic resonance imaging parameter changes as an early biomarker of tumor responses following radiation therapy in patients with spinal metastases: a systematic review

PURPOSE

This systematic review aims to assess and summarize the clinical values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameter changes as early biomarkers of tumor responses following radiation therapy (RT) in patients with spinal metastases.

MATERIALS AND METHODS

A systematic search was conducted on five electronic databases: PubMed, Scopus, Science Direct, Cochrane, and Embase. Studies were included if they mentioned DCE-MRI parameter changes before and after RT in patients with spinal metastases with a correlation to tumor responses based on clinical and imaging criteria. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess study quality.

RESULTS

This systematic review included seven studies involving 107 patients. All seven studies evaluated the transfer constant (Ktrans), six studies evaluated the plasma volume fraction (Vp), three studies evaluated the extravascular extracellular space volume fraction, and two studies evaluated the rate constant. There were variations in the type of primary cancer, RT techniques used, post-treatment scan time, and median follow-up time. Despite the variations, however, the collected evidence generally suggested that significant differences could be detected in DCE-MRI parameters between before and after RT, which might reflect treatment success or failures in long-term follow-up. Responders showed higher reduction and lower values of Ktrans and Vp after RT. DCE-MRI parameters showed changes and detectable recurrences significantly earlier (up to 6 months) than conventional MRI with favorable diagnostic values.

CONCLUSION

The results of this systematic review suggested that DCE-MRI parameter changes in patients with spinal metastases could be a promising tool for treatment-response assessment following RT. Lower values and higher reduction of Ktrans and Vp after treatment demonstrated good prediction of local control. Compared to conventional MRI, DCE-MRI showed more rapid changes and earlier prediction of treatment failure.

H-Scan Ultrasound Monitoring of Breast Cancer Response to Chemotherapy and Validation With Diffusion-Weighted Magnetic Resonance Imaging

OBJECTIVES

H-scan ultrasound (US) imaging is a novel tissue characterization technique to detect apoptosis-induced changes in cancer cells after the initiation of effective drug treatment. The objective of the proposed research was to assess the sensitivity of 3-dimensional (3D) H-scan US technique for monitoring the response of breast cancer-bearing animals to neoadjuvant chemotherapy and correlate results to diffusion-weighted magnetic resonance imaging (DW-MRI) measurements of programmed cancer cell death.

METHODS

Experimental studies used female mice (N = 18) implanted with human breast cancer cells. Animals underwent H-scan US and DW-MRI imaging on days 0, 1, 3, 7, and 10. After imaging at day 0, breast tumor-bearing nude mice were treated biweekly with an apoptosis-inducing drug. Texture analysis of H-scan US images explored spatial relationships between local US scattering. At day 10, H-scan measurements were compared with DW-MRI-derived apparent diffusion coefficient (ADC) values and histological findings.

RESULTS

H-scan US imaging of low and high dose cisplatin-treated cancer-bearing animals revealed changes in image intensity suggesting a progressive decrease in aggregate US scatterer size that was not observed in control animals. Longitudinal trends discovered in H-scan US result matched with texture analysis and DW-MRI (P < .01). Further, analysis of the H-scan US image intensity and corresponding DW-MRI-derived ADC values revealed a strong linear correlation (R²  = .93, P < .001). These changes were due to cancer cell apoptotic activity and consider as early detectable biomarker during treatment.

CONCLUSIONS

The 3D H-scan technique holds promise for assisting clinicians in monitoring the early response of breast cancer tumor to neoadjuvant chemotherapy and adding value to traditional diagnostic ultrasound examinations.

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer

PURPOSE

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that K^trans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known.

PROCEDURES

Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment.

RESULTS

Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, k_ep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when k_ep is obtained from fitting the DCE-MRI data using the SSM, k_ep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of k_ep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when k_ep is derived from the TM, only the use of individual AIF leads to a significant increase of k_ep after PEGPH20 treatment, whereas the %change of k_ep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of V_p (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of V_p is consistent with a large increase of mean capillary lumen area estimated from immunostaining.

CONCLUSION

Our results suggest that k_ep derived from SSM and V_p from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

Registration on DCE-MRI images via multi-domain image-to-image translation

Registration of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging as rapid intensity changes caused by a contrast agent lead to large registration errors. To address this problem, we propose a novel multi-domain image-to-image translation (MDIT) network based on image disentangling for separating motion from contrast changes before registration. In particular, the DCE images are disentangled into a domain-invariant content space (motion) and a domain-specific attribute space (contrast changes). The disentangled representations are then used to generate images, where the contrast changes have been removed from the motion. After that the resulting deformations can be directly derived from the generated images using an FFD registration. The method is tested on 10 lung DCE-MRI cases. The proposed method reaches an average root mean squared error of 0.3 ± 0.41 and the separation time is about 2.4 s for each case. Results show that the proposed method improves the registration efficiency without losing the registration accuracy compared with several state-of-the-art registration methods.

Evaluation of pretreatment ADC values as predictors of treatment response to neoadjuvant chemotherapy in patients with breast cancer - a multicenter study

BACKGROUND

Magnetic resonance imaging (MRI) can be used to diagnose breast cancer. Diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can reflect tumor microstructure in a non-invasive manner. The correct prediction of response of neoadjuvant chemotherapy (NAC) is crucial for clinical routine. Our aim was to compare ADC values between patients with pathological complete response (pCR) and non-responders based upon a multi-center design to improve the correct patient selection, which patient would more benefit from NAC and which patient would not.

METHODS

For this study, data from 4 centers (from Japan, Brazil, Spain and United Kingdom) were retrospectively acquired. The time period was overall 2003-2019. The patient sample comprises 250 patients (all female; median age, 50.5). In every case, pretreatment breast MRI with DWI was performed. pCR was assessed by experienced pathologists in every center using the surgical specimen in the clinical routine work up. pCR was defined as no residual invasive disease in either breast or axillary lymph nodes after NAC. ADC values between the group with pCR and those with no pCR were compared using the Mann-Whitney U test (two-group comparisons). Univariable and multivariabe logistic regression analysis was performed to predict pCR status.

RESULTS

Overall, 83 patients (33.2%) achieved pCR. The ADC values of the patient group with pCR were lower compared with patients without pCR (0.98 ± 0.23 × 10^- 3 mm²/s versus 1.07 ± 0.24 × 10^- 3 mm²/s, p = 0.02). The ADC value achieved an odds ratio of 4.65 (95% CI 1.40-15.49) in univariable analysis and of 3.0 (95% CI 0.85-10.63) in multivariable analysis (overall sample) to be associated with pCR status. The odds ratios differed in the subgroup analyses in accordance with the molecular subtype.

CONCLUSIONS

The pretreatment ADC-value is associated with pathological complete response after NAC in breast cancer patients. This could aid in clinical routine to reduce treatment toxicity for patients, who would not benefit from NAC. However, this must be tested in further studies, as the overlap of the ADC values in both groups is too high for clinical prediction.

Predicting the Early Response to Neoadjuvant Therapy with Breast MR Morphological, Functional and Relaxometry Features-A Pilot Study

Aim: To evaluate the role of MR relaxometry and derived proton density analysis in the prediction of early treatment response after two cycles of neoadjuvant therapy (NAT), in patients with breast cancer. Methods: This was a prospective study that included 59 patients with breast cancer, who underwent breast MRI prior (MRI1) and after two cycles of NAT (MRI2). The MRI1 included a sequential acquisition with five different TE’s (50, 100, 150, 200 and 250 ms) and a TR of 5000 ms. Post-processing was used to obtain the T2 relaxometry map from the MR acquisition. The tumor was delineated and seven relaxometry and proton density parameters were extracted. Additional histopathology data, T2 features and ADC were included. The response to NAT was reported based on the MRI2 as responders: partial response (>30% decreased size) and complete response (no visible tumor stable disease (SD); and non-responders: stable disease or progression (>20% increased size). Statistics was done using Medcalc software. Results: There were 50 (79.3%) patients with response and 13 (20.7%) non-responders to NAT. Age, histologic type, “in situ” component, tumor grade, estrogen and progesterone receptors, ki67% proliferation index and HER2 status were not associated with NAT response (all p > 0.05). The nodal status (N) 0 was associated with early response, while N2 was associated with non-response (p = 0.005). The tumor (T) and metastatic (M) stage were not statistically significant associated with response (p > 0.05). The margins, size and ADC values were not associated with NAT response (p-value > 0.05). The T2 min relaxometry value was associated with response (p = 0.017); a cut-off value of 53.58 obtained 86% sensitivity (95% CI 73.3−94.2), 69.23 specificity (95% CI 38.6−90.9), with an AUC = 0.715 (p = 0.038). The combined model (T2 min and N stage) achieved an AUC of 0.826 [95% CI: 0.66−0.90, p-value < 0.001]. Conclusions: MR relaxometry may be a useful tool in predicting early treatment response to NAT in breast cancer patients.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics

OBJECTIVE

This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.

METHODS

Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity.

RESULTS

With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17).

CONCLUSION

A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis.

SIGNIFICANCE

Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Predictive value of DCE-MRI and IVIM-DWI in osteosarcoma patients with neoadjuvant chemotherapy

Objective

To investigate the predictive value of dynamic contrast enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for clinical outcomes of osteosarcoma patients with neoadjuvant chemotherapy.

Methods

The present prospective single-arm cohort study enrolled 163 patients of osteosarcoma during July 2017 to July 2022. All patients received the same treatment strategy of neoadjuvant chemotherapy. Both DCE-MRI and IVIM-DWI were conducted for the patients before the chemotherapy, as well as after one or two chemotherapy treatment cycles. The imaging parameters of contrast agent transfer rate between blood and tissue (K^trans), contrast agent back-flux rate constant (K_ep), extravascular extracellular fractional volume (V_e), as well as pure diffusion coefficient (D value), pseudo-diffusion coefficient (D* value), apparent diffusion coefficient (ADC) and the perfusion fraction (f value) were recorded. RECIST standard [complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD)] was used as the main clinical outcome.

Results

After two treatment cycles, 112 (68.71%) cases were with CR and PR, 31 (19.02%) cases were with SD and 20 cases (12.27%) were with PD. After 1~2 treatment cycles, patients with CR/PR showed significantly markedly lower K^trans, K_ep, V_e values, while higher D, ADC and f values compared with SD or PD patients. Alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were positively correlated with values of K^trans, K_ep, and V_e, while negative correlation was observed between ALP and values of D, ADC and f, as well as between LDH and D and ADC after the whole treatment. D and K_ep values after two treatment cycles showed the best predictive value for diagnosis of PD. The values of K^tran, K_ep, ADC as well as ALP and LDH were all risk factors for PD after neoadjuvant chemotherapy.

Conclusion

DCE-MRI and IVIM-DWI have the potential to predict clinical outcomes of osteosarcoma patients with neoadjuvant chemotherapy.

Clinical N3 is an independent risk factor of recurrence for breast cancer patients achieving pathological complete response and near-pathological complete response after neoadjuvant chemotherapy

Background

Although achieving pathological complete response (pCR) and near-pathological complete response (near-pCR) after neoadjuvant chemotherapy (NAC) in breast cancer predicts a better outcome, some patients still experience recurrence. The aim of our study was to investigate the predictive factors of recurrence in the pCR and near-pCR population.

Methods

We reviewed 1,209 breast cancer patients treated with NAC between January 2010 and April 2021 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS). A total of 292 patients achieving pCR and near-pCR were included in our analysis. pCR was defined as ypT0N0/ypTisN0. Near-pCR was defined as ypT1mi/1a/1bN0 or ypT0/isN1mi. Clinical features and follow-up information were collected. Survival and predictive factors of recurrence were analyzed.

Results

Of the 292 patients, 173 were pCR and 119 were near-pCR. The median age was 46 years (range, 23–75 years). The predominant tumor subtypes were human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (49.0%) and triple-negative breast cancer (TNBC) (30.8%). The median duration of follow-up was 53 months (range, 9–138 months). A total of 25 (8.6%) patients developed recurrence, with 9 (5.2%) in the pCR group and 16 (13.4%) in the near-pCR group. The vast majority of recurrence occurred within 36 months from onset of NAC. The 5-year recurrence-free survival (RFS) rate of patients achieving pCR was significantly higher than that of patients achieving near-pCR (94.6% vs. 85.6%, p = 0.008). However, the 5-year overall survival (OS) rate between the two cohorts had no statistical difference (94.3% vs. 89.6%, p = 0.304). Clinical N3 (cN3) before NAC was an independent risk factor of recurrence in patients who achieved pCR (p = 0.003) and near-pCR (p = 0.036). Tumor size before NAC, subtypes of breast cancer, and chemotherapy regimens showed no significant association with RFS both for patients who achieved pCR and for those who achieved near-pCR (p > 0.05).

Conclusions

cN3 before NAC was an independent risk factor of recurrence in patients who achieved pCR and near-pCR. It is worthwhile to closely monitor patients with cN3, especially in the first 3 years.

Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: protocol of the LIMA study - a multicentre prospective observational cohort study

INTRODUCTION

The response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment.

AIM

To combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC.

METHODS

In this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques.

ETHICS AND DISSEMINATION

Medical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04223492.

MRI to assess response after neoadjuvant chemotherapy in breast cancer subtypes: a systematic review and meta-analysis

This meta-analysis aimed to estimate and compare sensitivity, specificity, positive- (PPV) and negative predictive value (NPV) of magnetic resonance imaging (MRI) for predicting pathological complete remission (pCR) after neoadjuvant chemotherapy (NAC) in patients with early-stage breast cancer. We stratified for molecular subtype by immunohistochemistry (IHC) and explored the impact of other factors. Two researchers systematically searched PUBMED and EMBASE to select relevant studies and extract data. For meta-analysis of sensitivity and specificity, we used bivariate random-effects models. Twenty-six included studies contained 4497 patients. There was a significant impact of IHC subtype on post-NAC MRI accuracy (p = 0.0082) for pCR. The pooled sensitivity was 0.67 [95% CI 0.58-0.74] for the HR-/HER2-, 0.65 [95% CI 0.56-0.73] for the HR-/HER2+, 0.55 [95% CI 0.45-0.64] for the HR+/HER2- and 0.60 [95% CI 0.50-0.70] for the HR+/HER2+ subtype. The pooled specificity was 0.85 [95% CI 0.81-0.88] for the HR-/HER2-, 0.81 [95% CI 0.74-0.86] for the HR-/HER2+, 0.88[95% CI 0.84-0.91] for the HR+/HER2- and 0.74 [95% CI 0.63-0.83] for the HR+/HER2+ subtype. The PPV was highest in the HR-/HER2- subtype and lowest in the HR+/HER2- subtype. MRI field strength of 3.0 T was associated with a higher sensitivity compared to 1.5 T (p = 0.00063). The accuracy of MRI for predicting pCR depends on molecular subtype, which should be taken into account in clinical practice. Higher MRI field strength positively impacts accuracy. When intervention trials based on MRI response evaluation are designed, the impact of IHC subtype and field strength on MR accuracy should be considered.

Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging

Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group ( $P<0.05$ ), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group ( $P<0.05$ ). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference ( $P>0.05$ ). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI

OBJECTIVE

To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume.

METHODS

This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression.

RESULTS

A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively.

CONCLUSION

Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer

OBJECTIVES

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.

RESULTS

Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.

CONCLUSIONS

Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.

KEY POINTS

• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Plasma Cell-Free DNA Integrity Assessed by Automated Electrophoresis Predicts the Achievement of Pathologic Complete Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer

PURPOSE

The study of plasma cell-free DNA integrity (cfDI) has shown potential for providing useful information in neoplastic patients. The aim of this study is to estimate the accuracy of an electrophoresis-based method for cfDI evaluation in the assessment of pathologic complete response (pCR) in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NACT).

PATIENTS AND METHODS

Fifty-one patients with BC undergoing anthracycline-/taxane-based NACT were recruited. Plasma samples were collected from each patient at diagnosis (t0), after anthracycline administration (t1), and after NACT completion (t2). The concentration of differently sized cell-free DNA fragments was assessed by automated electrophoresis. cfDI, expressed as cfDI index, was calculated as the ratio of 321-1,000 bp sized fragment concentration to 150-220 bp sized fragment concentration assessed at t2. cfDI index was then used to build an exploratory classifier for BC response to NACT, directly comparing its sensitivity and specificity with magnetic resonance imaging (MRI), through bootstrapped logistic regression.

RESULTS

cfDI index was assessed on 38 plasma samples collected from as many patients at t2, maintaining a 30/70 ratio between pCR and non-pCR patients. cfDI index showed an area under the receiver operating characteristic curve in predicting the achievement of pCR of 81.6, with a cutoff above 2.71 showing sensitivity = 81.8 and specificity = 81.5. The combination of cfDI index and MRI showed, in case of concordance, an area under the receiver operating characteristic curve of 92.6 with a predictive value of complete response of 87.5 and a predictive value of absence of complete response of 94.7.

CONCLUSION

cfDI index measured after NACT completion shows great potential in the assessment of pCR in patients with BC. The evaluation of its use in combination with MRI is strongly warranted in prospective studies.

Plasma DNA fragmentation assessment predicts response to neoadjuvant chemotherapy in breast cancer.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Role of MRI in predicting response to neo-adjuvant systemic therapy (NAST) in breast cancer

BACKGROUND AND OBJECTIVES

MRI is generally performed to assess response to Neo-adjuvant systemic therapy (NAST) in breast cancer.

OBJECTIVE

To assess role of MRI in determining the probability of having residual disease in patients undergoing NAST. We also evaluated synchronous cancers diagnosed following MRI.

METHODS

This is a retrospective study which included all patients who had pre-and post-NAST MRI between June 2014 and December 2019. Data on demographics, tumour characteristics and pathology were collected and analysed. Pre- and post-MRI probability were calculated and depicted on nomograms.

RESULTS

The study included 205 patients. Overall pre-MRI probability of having residual disease was 55% (OR:1.2). The post-MRI probability was 78% (95% CI 72-83%; OR:3.5) if MRI showed residual disease and 23% (95% CI 16-31%, OR:0.3) if imaging showed complete response. The absolute benefit was higher in TNBC and HR-HER2. Additional cancers were identified in 8.78% of patients.

CONCLUSION

MRI is beneficial in evaluating response to NAST specifically in TNBC and HR-HER2 cancers. Pre- and post-MRI probabilities of residual disease depicted on nomograms are a useful tool for clinicians. MRI can potentially impact the treatment decisions by identification of synchronous cancers.

Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer

Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.

Hyperpolarized Carbon-13 MRI for Early Response Assessment of Neoadjuvant Chemotherapy in Breast Cancer Patients

Hyperpolarized 13C-MRI is an emerging tool for probing tissue metabolism by measuring 13C-label exchange between intravenously injected hyperpolarized [1-13C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized 13C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer. Seven patients underwent multiparametric 1H-MRI and hyperpolarized 13C-MRI before and 7-11 days after commencing treatment. An increase in the lactate-to-pyruvate ratio of approximately 20% identified three patients who, following 5-6 cycles of treatment, showed pathological complete response. This ratio correlated with gene expression of the pyruvate transporter MCT1 and lactate dehydrogenase A (LDHA), the enzyme catalyzing label exchange between pyruvate and lactate. Analysis of approximately 2,000 breast tumors showed that overexpression of LDHA and the hypoxia marker CAIX was associated with reduced relapse-free and overall survival. Hyperpolarized 13C-MRI represents a promising method for monitoring very early treatment response in breast cancer and has demonstrated prognostic potential. SIGNIFICANCE: Hyperpolarized carbon-13 MRI allows response assessment in patients with breast cancer after 7-11 days of neoadjuvant chemotherapy and outperformed state-of-the-art and research quantitative proton MRI techniques.

Are we choosing wisely? Drivers of preoperative MRI use in breast cancer patients

INTRODUCTION

Factors contributing to the use of preoperative MRI remain poorly understood.

METHODS

Data from a randomized controlled trial of stage 0-3 breast cancer patients undergoing breast conserving surgery between 2016 and 2018 were analyzed.

RESULTS

Of the 396 patients in this trial, 32.6% had a preoperative MRI. Patient age, race, ethnicity, tumor histology, and use of neoadjuvant therapy were significant predictors of MRI use. On multivariate analysis, younger patients with invasive lobular tumors were more likely to have a preoperative MRI. Rates also varied significantly by individual surgeon (p < 0.001); in particular, female surgeons (39.9% vs. 24.0% for male surgeons, p = 0.001) and those in community practice (58.9% vs. 14.2% for academic, p < 0.001) were more likely to order preoperative MRI. Rates declined over the two years of the study, particularly among female surgeons.

CONCLUSIONS

Preoperative MRI varies with patient age and tumor histology; however, there remains variability by individual surgeon.

The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis

PURPOSE

To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC).

METHODS

We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis.

RESULTS

A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) ​​between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity.

CONCLUSIONS

DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.

Radiological and pathological assessment of response to neoadjuvant CDK4/6 inhibitor and endocrine treatments in a real-life setting-initial results

BACKGROUND

Neoadjuvant endocrine therapy is an alternative to neoadjuvant chemotherapy in women with inoperable luminal-like breast cancers. Neoadjuvant cyclin-dependent kinase 4/6 inhibitor treatment combined with endocrine treatment (CDK4/6I + E) is interesting given the combination's utility in the treatment of metastatic breast cancer. Currently, the literature on the radiological response evaluation of patients treated with neoadjuvant CDK4/6I + E in a real-life setting is scarce.

PURPOSE

To conduct a radiological response evaluation of patients treated with neoadjuvant CDK4/6I + E in a real-life setting.

MATERIAL AND METHODS

We retrospectively reviewed clinical, pathological, and radiological findings of six patients with luminal-like breast cancers treated with neoadjuvant CDK4/6I + E treatment. The radiological neoadjuvant CDK4/6I + E response was evaluated with the RECIST 1.1 criteria and the pathological residual disease was assessed using the Residual Cancer Burden (RBC) criteria.

RESULTS

None of the patients achieved a complete radiological magnetic resonance imaging (MRI)-determined response or a complete pathological response; three (50%) patients had a partial radiological response; in the three others, the disease remained stable radiologically. All of the tumors were rendered susceptible to surgical treatment. Two out of six (33.3%) patients had a moderate response (RBC-II); four (66.7%) had an extensive residual disease (RBC-III) in the final surgical sample.

CONCLUSION

Although none of the patients achieved a pathologically complete response, neoadjuvant CDK4/6I + E treatment rendered all tumors operable. MRI appears to be reliable in the assessment of the neoadjuvant CDK4/6I + E treatment response in a real-life setting. Larger studies are warranted to confirm these results.

Breast tumour volume and blood flow measured by MRI after one cycle of epirubicin and cyclophosphamide-based neoadjuvant chemotherapy as predictors of pathological response

OBJECTIVES

Better markers of early response to neoadjuvant chemotherapy (NACT) in patients with breast cancer are required to enable the timely identification of non-responders and reduce unnecessary treatment side-effects. Early functional imaging may better predict response to treatment than conventional measures of tumour size. The purpose of this study was to test the hypothesis that the change in tumour blood flow after one cycle of NACT would predict pathological response.

METHODS

In this prospective cohort study, dynamic contrast-enhanced MRI was performed in 35 females with breast cancer before and after one cycle of epirubicin and cyclophosphamide-based NACT (EC90). Estimates of tumour blood flow and tumour volume were compared with pathological response obtained at surgery following completion of NACT.

RESULTS

Tumour blood flow at baseline (mean ± SD; 0.32 ± 0.17 ml/min/ml) reduced slightly after one cycle of NACT (0.28 ± 0.18 ml/min/ml). Following treatment 15 patients were identified as pathological responders and 20 as non-responders. There were no relationships found between tumour blood flow and pathological response. Conversely, tumour volume was found to be a good predictor of pathological response (smaller tumours did better) at both baseline (area under the receiver operating characteristic curve 0.80) and after one cycle of NACT (area under the receiver operating characteristic curve 0.81).

CONCLUSION & ADVANCES IN KNOWLEDGE

The change in breast tumour blood flow following one cycle of EC90 did not predict pathological response. Tumour volume may be a better early marker of response with such agents.

The use of breast ultrasound for prediction of pathologic complete response in different subtypes of early breast cancer within the WSG-ADAPT subtrials

OBJECTIVE

We assessed the value of breast ultrasound (US) performed at week 3 and 6 and at the end (EOT) of neoadjuvant therapy (NAT) for prediction of pathologic complete response (pCR, ypT0/is ypN0) in patients with HR+/HER2+, HR-/HER2-or HR-/HER2+ early breast cancer enrolled in the WSG-ADAPT subtrials.

METHODS

US was performed at week 3 and 6 of NAT and at EOT in 401, 517, and 553 patients, respectively. Tumors with complete or partial response by US (RECIST 1.1) were classified as responders and those with stable or progressive disease as non-responders.

RESULTS

pCR rate was higher in US responders than in non-responders. US tended to yield the highest positive predictive value in HR-/HER2+ (69%) and HR-/HER2-tumors (65%) at week 3, and the highest negative predictive value in HR+/HER2+ tumors at week 6 and at EOT (88.9% and 86.9%, respectively) and in HR-/HER2-tumors at EOT (87.9%). Multivariable analysis of patients with US at week 3 and 6 identified tumor subtype (HR-/HER2+ vs HR+/HER2+; odds ratio (OR) 2.77, 95%CI 1.45-5.29, and OR 4.17, 95%CI 2.26-7.68, respectively) and each 10% change in lesion dimension on US from baseline (OR 1.15, 95%CI 1.08-1.24, and OR 1.25, 95%CI 1.16-1.35, respectively) as parameters associated with pCR.

CONCLUSIONS

Our data support the use of week 3 and EOT US for prediction of pCR in response-guided NAT and in planning of breast-conserving surgery. Change in tumor diameter on US as a continuous variable could be a valuable alternative to categorical RECIST 1.1 criteria.

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = -1.29, P < 0.001) and ADC (SMD = -1.21, P < 0.001) than benign tumors. The MK (SMD = -1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis

Objectives: The diagnostic performance of intravoxel incoherent motion diffusion–weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors.

Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities.

Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = −1.38, P < 0.001) and D values (SMD = −1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D^* value (SMD = −0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D^* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D^* values (sensitivity = 84%, specificity = 59%, AUC = 0.71).

Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.

Breast Ultrasound Versus MRI in Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy for Breast Cancer: A Systematic Review and Meta-Analysis

Introduction:

Neoadjuvant chemotherapy (NAC) is widely used to treat breast cancer. Sentinel lymph node biopsy has replaced axillary lymph node dissection in patients who convert to node-negative status, after NAC. However, few studies have evaluated the diagnostic performance of ultrasonography (US) and magnetic resonance imaging (MRI) in determining axillary lymph node status after NAC. The aim of this study was to evaluate the diagnostic performance of breast US and MRI in predicting a response to NAC, for breast cancer.

Methods:

A systematic search, in PubMed, the Cochrane Library, and Web of Science, for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data were used to reconstruct 2 × 2 tables. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis, and sensitivity analysis were performed using Meta-DiSc and Stata version 14.0 (StataCorp LP, College Station, TX, USA).

Results:

Nine studies met all the eligibility criteria and were included. The pooled sensitivity and specificity of MRI were 0.78 and 0.92, while the corresponding values for US were 0.80 and 0.90, respectively. The prevalence of pathologic complete response (pCR), among breast cancer patients, after neoadjuvant therapy was 26%. The prevalence of patients with estrogen receptor (ER)-, human epidermal growth factor receptor (HER)-, and progesterone receptor (PR)-positive tumors were 65%, 22%, and 37%, respectively.

Conclusion:

These results showed that MRI and US have almost the same accuracy in predicting pCR in patients with breast cancer undergoing neoadjuvant surgery. There is still a need for further investigations to prove that US is not inferior to MRI for this diagnosis.

Early response and pathological complete remission in Breast Cancer with different molecular subtypes: a retrospective single center analysis

Purpose: To evaluate the association of clinical early response and pathological complete remission (pCR) in breast cancer patients with different molecular subtypes.

Materials and methods: Breast cancer patients who received neoadjuvant treatment (NAT) with clinical early response assessment from October 2008 to October 2018 were retrospectively analyzed. Clinical early response was defined as tumor size decreasing ≥30% evaluated by ultrasound after two cycles of NAT. Chi-square test was used to compare the pCR rates between the responder and non-responder groups with different molecular subtypes. Multivariate logistic regression was used to identify independent factors associated with the pCR.

Results: A total of 328 patients were included: 100 responders and 228 non-responders. The progesterone receptor (PR) expression was an independent factor associated with clinical early response (OR=2.39, 95%CI=1.41-4.05, P=0.001). The pCR rate of breast was 50.0% for responders and 18.0% for non-responders (P<0.001). Regarding different molecular subtypes, responders had higher pCR rates than non-responders for patients with HER2 overexpression (OR=10.66, 95%CI=2.18-52.15, P=0.001), triple negative (OR=3.29, 95%CI=1.23-8.84, P=0.016) and Luminal (HER2-) subtypes (OR=8.58, 95%CI=3.05-24.10, P<0.001) respectively. Moreover, pCR rate can be achieved as high as 88.2% in HER2 overexpression patients with early clinical response, which was significantly higher than patients without early response (41.3%, P=0.001). Multivariate analysis showed that clinical early response was an independent factor associated with the pCR rate (OR=4.87, 95%CI=2.72-8.72, P<0.001).

Conclusions: Early response was significantly associated with a higher pCR rate in breast cancer patients receiving NAT, especially for patients with HER2 overexpression subtype, which warrants further clinical evaluation.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis

Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa.

Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities.

Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D^* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D^*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D^* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76).

Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

Background and objectives

The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method.

Materials and methods

The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities.

Results

Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66).

Conclusion

IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.

The diagnostic performance of CESM and CE-MRI in evaluating the pathological response to neoadjuvant therapy in breast cancer: a systematic review and meta-analysis

OBJECTIVES

Neoadjuvant chemotherapy (NAC) is an important method for breast cancer treatment. By monitoring its pathological response, the selection of clinical treatment strategies can be guided. In this study, the meta-analysis was used to compare the accuracy of contrast-enhanced MRI (CE-MRI) and contrast-enhanced spectral mammography (CESM) in detecting the pathological response of NAC.

METHODS

Literatures associated to CE-MRI and CESM in the evaluation of pathological response of NAC were searched from PubMed, Cochrane Library, web of science, and EMBASE databases. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the quality of studies. Pooled sensitivity, specificity, and the area under the SROC curve were calculated to evaluate the diagnostic accuracy of CE-MRI and CESM in monitoring the pathological response of NAC.

RESULTS

There were 24 studies involved, 18 of which only underwent CE-MRI examination, three of which only underwent CESM examination, and three of which underwent both CE-MRI and CESM examination. The pooled sensitivity and specificity of CE-MRI were 0.77 (95%CI, 0.67-0.84) and 0.82 (95%CI, 0.73-0.89), respectively. The pooled sensitivity and specificity of CESM were 0.83 (95%CI, 0.66-0.93) and 0.82 (95%CI, 0.68-0.91), respectively. The AUCs of SROC curve for CE-MRI and CESM were 0.86 and 0.89, respectively.

CONCLUSIONS

Compared to CE-MRI, CESM has equal specificity, greater sensitivity and excellent performance, which may have a brighter prospect in evaluating the pathological response of breast cancer to NAC.

ADVANCES IN KNOWLEDGE

CESM showed equal specificity, greater sensitivity, and excellent performance than CE-MRI.

The Diagnostic Performance of Minimally Invasive Biopsy in Predicting Breast Pathological Complete Response After Neoadjuvant Systemic Therapy in Breast Cancer: A Meta-Analysis

Background: Neoadjuvant systemic therapy (NST) is commonly used in patients with early stage breast cancer before definitive surgery. The standard diagnostic approach for pathologic complete response (pCR) of the breast is breast surgery and pathologic examination. In recent years, several trials investigated the predictive value of image-guided minimally invasive biopsy (MIB) for breast pCR after NST. This study conducted a meta-analysis to evaluate the diagnostic accuracy of MIB.

Materials and Methods: We identified relevant research reports in online databases through February 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to evaluate the quality of included trials. We extracted relevant data and constructed a 2 × 2 contingency table to analyze the predictive accuracy of MIB for breast pCR. Subgroup analyses and meta-regressions were also performed to investigate potential causes of heterogeneity.

Results: Nine trials (with 1,030 breast cancer patients) were included in this meta-analysis. The pooled sensitivity and specificity of MIB were 0.72 [95% confidence interval (CI): 0.61–0.81] and 0.99 (95% CI: 0.89–1.00), respectively. By combining relevant data, there were no significant differences in sensitivity or specificity among different molecular subtypes of breast cancer (P > 0.05). Subgroup analyses and meta-regressions implied that trials with responses not limited to clinical complete response (cCR) had a significantly higher accuracy of MIB than those with only cCR (RDOR: 7.65; 95% CI: 1.05–55.46; P = 0.046).

Conclusion: Current image-guided MIB methods are not accurate enough in terms of predicting breast pCR after NST. It is of utmost clinical importance to standardize the MIB procedure and incorporate other factors into the evaluation in order to improve the accuracy to an acceptable level.