MRI vs. histologic measurement of breast cancer following chemotherapy: comparison with x-ray mammography and palpation

Preoperative estimation of the pathological breast tumor size in architectural distortions: a comparison of DM, DBT, US, CEM, and MRI

OBJECTIVE

This study aims to compare the accuracy of digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), magnetic resonance imaging (MRI), and contrast-enhanced mammography (CEM) in the preoperative evaluation of breast cancer size in architectural distortions (ADs). Additionally, it assesses whether including thin spicules in mammography measurements affects accuracy.

MATERIALS AND METHODS

We planned a retrospective analysis of invasive breast cancers presenting as ADs in our breast screening program between 2018 and 2022. Tumor size was measured in mm using DM, DBT, US, MRI, and CEM. Measurements were compared to the surgical specimen sizes. Two measurement approaches for DM and DBT were applied, considering and not considering thin spicules. T-student test was used to compare mean sizes across imaging techniques with the surgical specimen.

RESULTS

The study encompassed 59 female patients with 63 ADs. Mean age was 60.1 years (Standard Deviation (SD): 6.3). The cancers included four histological subtypes, ductal (69.8%), lobular (23.8%), tubular (4.8%), and micropapillary (1.6%). All imaging techniques, except for US (mean: 12.4 mm, SD: 5.7), overestimated tumor size compared to histology (mean: 16.40 mm, SD: 9). CEM, MRI, and DBT without thin spicules closely matched histological size. Including thin spicules in DM and DBT led to overestimation. Concordance was highest with CEM (75%) and MRI (67.6%). No significant differences were found between ductal and lobular carcinoma.

CONCLUSION

For preoperative tumor size estimation of breast cancer in ADs, DBT excluding thin spicules, CEM, and MRI seemed most accurate. Including thin spicules in mammography leads to overestimation.

KEY POINTS

Question Identifying the most accurate imaging technique for preoperative tumor staging of architectural distortions (ADs) is crucial now that contrast-enhanced mammography (CEM) is widely implemented. Findings Measuring thin wispy spicules in ADs on digital (DM) and digital breast tomosynthesis (DBT) should be avoided, as they consistently overestimate pathological tumor stage. Clinical relevance Precise tumor size estimation in ADs is critical for proper staging and treatment planning. This study favors the use of DBT excluding thin spicules, CEM, and magnetic resonance imaging (MRI) for optimal accuracy.

The Role of MRI in Assessing Residual Breast Cancer After Neoadjuvant Chemotherapy

INTRODUCTION

Breast cancer is the most common malignancy among women in the world. The role of neoadjuvant chemotherapy (NAC) in the management of breast cancer is increasing. The decision about the management after NAC depends mainly on the tumor response to NAC.

OBJECTIVES

The role of the current study is to evaluate the role of the MRI scan in assessing the residual disease after NAC, which would help in decision making regarding the best treatment plan for the patient.

PATIENTS AND METHODS

We did this retrospective review for all patients who were diagnosed with breast cancer in our center and had NAC over four years. All patients in our study had a post-NAC magnetic resonance imaging (MRI) scan to assess the residual tumor size. A 2×2 table was used to calculate the diagnostic accuracy, and SPSS software version 25 was used to get the correlation coefficients between the post-NAC MRI measurements and pathological size.

RESULTS

28 female patients were included in our study. The average age was 45.25 ± 10 years. We utilized the tumor size on histology as the standard for comparison. We calculated MRI sensitivity, specificity, PPV, and NPV rates of 90.9%, 100%, 100%, and 94.4%, respectively. The correlation coefficient was strong (r = 0.859, P = 0.01).

CONCLUSION

Magnetic resonance imaging is a good test to assess the residual tumor disease after NAC in breast cancer patients. However, cases of under- and overestimation are still seen, which require more caution when making a decision regarding the management of such cases.

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI

Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.

Patient perspectives on repeated contrast-enhanced mammography and magnetic resonance during neoadjuvant chemotherapy of breast cancer

BACKGROUND

The burden perceived by the patient of repeated imaging required for neoadjuvant chemotherapy (NAC) monitoring warrants attention due to the increased use of NAC and imaging.

PURPOSE

To evaluate and compare the experienced burden associated with repeated contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) during NAC for breast cancer from the patient perspective.

MATERIAL AND METHODS

Approval from the ethics committee and written informed consent were obtained. In this prospective study, CEM and MRI were performed on 38 patients with breast cancer before, during, and after NAC in a tertiary cancer center. The experienced burden was evaluated with a self-reported questionnaire addressing duration, comfort, anxiety, positioning, and intravenous contrast administration, each measured on a 5-point Likert scale. The participants were asked their preference between CEM or MRI. Statistical comparisons were performed and P<0.05 was considered significant.

RESULTS

Most participants (n = 29, 76%) preferred CEM over MRI (P = 0.0008). CEM was associated with a significantly shorter duration (P < 0.001), greater overall comfort (P < 0.01), more comfortable positioning (P = 0.01), and lower anxiety (P = 0.03). Intravenous contrast administration perception revealed no significant difference. Only 4 (10%) participants preferred MRI over CEM, due to the absence of breast compression.

CONCLUSION

In the hypothetical scenario of equal diagnostic accuracy, most participants preferred CEM and compared CEM favorably to MRI in all investigated features at repeated imaging required for NAC response assessment. Our results indicate that repeated examinations with CEM is well tolerated and constitutes a patient-friendly alternative for NAC imaging monitoring in breast cancer.

MRI in the Setting of Neoadjuvant Treatment of Breast Cancer

Neoadjuvant therapy may reduce tumor burden preoperatively, allowing breast conservation treatment for tumors previously unresectable or requiring mastectomy without reducing disease-free survival. Oncologists can also use the response of the tumor to neoadjuvant chemotherapy (NAC) to identify treatment likely to be successful against any unknown potential distant metastasis. Accurate preoperative estimations of tumor size are necessary to guide appropriate treatment with minimal delays and can provide prognostic information. Clinical breast examination and mammography are inaccurate methods for measuring tumor size after NAC and can over- and underestimate residual disease. While US is commonly used to measure changes in tumor size during NAC due to its availability and low cost, MRI remains more accurate and simultaneously images the entire breast and axilla. No method is sufficiently accurate at predicting complete pathological response that would obviate the need for surgery. Diffusion-weighted MRI, MR spectroscopy, and MRI-based radiomics are emerging fields that potentially increase the predictive accuracy of tumor response to NAC.

Feasibility of quantitative and volumetric enhancement measurement to assess tumor response in patients with breast cancer after early neoadjuvant chemotherapy

Objective

To evaluate the feasibility of quantitative enhancing lesion volume (ELV) for evaluating the responsiveness of breast cancer patients to early neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

Seventy-five women with breast cancer underwent DCE-MRI before and after NAC. Lesions were assessed by ELV, response evaluation criteria in solid tumors 1.1 (RECIST 1.1), and total lesion volume (TLV). The diagnostic and pathological predictive performances of the methods were compared and color maps were compared with pathological results.

Results

ELV identified 29%, 67%, and 4% of cases with partial response, stable disease, and progressive disease, respectively. There was no significant difference in evaluation performances among the methods. The sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of ELV for predicting pathologic response were 72%, 92%, 81.8%, 86.8%, and 85.3%, respectively, with the highest sensitivity, NPV, and accuracy of the three methods. The area under the receiver operating characteristic curve was also highest for ELV. Pre- and post-NAC color maps reflecting tumor activity were consistent with pathological necrosis.

Conclusions

ELV may help evaluate the responsiveness of breast cancer patients to NAC, and may provide a good tumor-response indicator through the ability to indicate tumor viability.

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Purpose

To assess the accuracy of magnetic resonance imaging (MRI) measurements in locally advanced oestrogen receptor-positive and human epidermal growth factor receptor 2-negative breast tumours before, during and after neoadjuvant endocrine treatment (NET) for evaluation of tumour response in comparison with clinical and pathological assessments.

Methods

This prospective study enrolled postmenopausal patients treated neoadjuvant with letrozole and exemestane given sequentially in an intra-patient cross-over regimen. Fifty-four patients were initially recruited, but only 35 fulfilled the inclusion criteria and confirmed to participate with a median age of 77. Tumours were scanned with MRI prior to treatment, during the eighth week of treatment and prior to surgery. Additionally, changes in longest diameter on clinical examination (CE) and tumour size at pathology were determined. Pre- and post-operative measurements of tumour size were compared in order to evaluate tumour response.

Results

The correlation between post-treatment MRI size and pathology was moderate and higher with a correlation coefficient (r) 0.64 compared to the correlation between CE and pathology r = 0.25. Post-treatment MRI and clinical results had a negligible bias towards underestimation of lesion size. Tumour size on MRI and CE had 0.82 cm and 0.52 cm lower mean size than tumour size measured by pathology, respectively.

Conclusions

The higher correlation between measurements of residual disease obtained on MRI and those obtained with pathology validates the accuracy of imaging assessment during NET. MRI was found to be more accurate for estimating complete responses than clinical assessments and warrants further investigation in larger cohorts to validate this finding.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05852-7) contains supplementary material, which is available to authorized users.

Near-IR emissive rare-earth nanoparticles for guided surgery

Intraoperative image-guided surgery (IGS) has attracted extensive research interests in determination of tumor margins from surrounding normal tissues. Introduction of near infrared (NIR) fluorophores into IGS could significantly improve the in vivo imaging quality thus benefit IGS. Among the reported NIR fluorophores, rare-earth nanoparticles exhibit unparalleled advantages in disease theranostics by taking advantages such as large Stokes shift, sharp emission spectra, and high chemical/photochemical stability. The recent advances in elements doping and morphologies controlling endow the rare-earth nanoparticles with intriguing optical properties, including emission span to NIR-II region and long life-time photoluminescence. Particularly, NIR emissive rare earth nanoparticles hold advantages in reduction of light scattering, photon absorption and autofluorescence, largely improve the performance of nanoparticles in biological and pre-clinical applications. In this review, we systematically compared the benefits of RE nanoparticles with other NIR probes, and summarized the recent advances of NIR emissive RE nanoparticles in bioimaging, photodynamic therapy, drug delivery and NIR fluorescent IGS. The future challenges and promises of NIR emissive RE nanoparticles for IGS were also discussed.

Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer after Neoadjuvant Therapy

Objectives

MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging. Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer.

Materials and Methods

A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled. On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software. Groups were divided according to the operational pathology. Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula. Overall diagnostic abilities were compared between TA and radiologists' subjective judgments. Validation was performed on a time-independent cohort of 39 consecutive patients.

Results

TA features with high consistency (Cronbach's alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups. Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists' subjective judgment (AUC = 0.72), and entropy was an independent risk factor (P < 0.001). Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%. AUC of the validation cohort was 0.80.

Conclusions

TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer. Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.

Accuracy of breast magnetic resonance imaging in evaluating the response to neoadjuvant chemotherapy: a study of 310 cases at a cancer center

Objective

To evaluate the accuracy of magnetic resonance imaging (MRI) of the breasts in the identification of a pathological complete response in patients with breast cancer undergoing neoadjuvant chemotherapy (NAC).

Materials and Methods

This was a single-center, retrospective, observational study designed to validate a diagnostic test. The following variables were evaluated: age; results of the histological and immunohistochemical analysis of the biopsy; post-NAC MRI findings; and results of the histological analysis of the surgical specimen, using the residual cancer burden index. The radiological response, as assessed by MRI, was compared with the pathological response, as assessed by histological analysis of the surgical specimen (the gold standard method).

Results

We evaluated 310 tumors in 308 patients. The mean age of the patients was 47 years (range, 27-85 years). For identifying a pathological complete response, breast MRI had an overall accuracy of 79%, with a sensitivity of 75%, specificity of 83%, positive predictive value of 75%, and negative predictive value of 83%. When that accuracy was stratified by molecular subtype, it was best for the HER2 subtype, with a sensitivity and specificity of 82% and 89%, respectively, followed by the triple-negative subtype, with a sensitivity and specificity of 78% and 83%, respectively.

Conclusion

Breast MRI showed good accuracy in the prediction of a pathological complete response after NAC. The sensitivity and positive predictive value were highest for the HER2 and triple-negative subtypes.

A model study of 3-dimensional localization of breast tumors using piezoelectric fingers of different probe sizes

Mammography is the only Food and Drug Administration approved breast cancer screening method. The drawback of the tumor image in a mammogram is the lack of tumor depth information as it is only a 2-dimensional projection of a 3-dimensional (3D) tumor. In this work, we investigated 3D tumor imaging by assessing tumor depth information using a set of piezoelectric fingers (PEFs) with different probe sizes which were known to be capable of eliciting tissue elastic responses to different depths and tested it on model tumor tissues consisted of gelatin with suspended clay inclusions. The locations of the top and bottom surfaces of an inclusion were resolved by solving a simple spring model using the elastic measurements of the PEFs of different probe sizes as the input. The lateral sizes of an inclusion were determined as the full width at half maximum of the Gaussian fit to the measured lateral tumor elastic modulus profile. The obtained lateral inclusion sizes were in close agreement with the actual values, and the deduced depth profiles of an inclusion also agreed with the actual depth profiles so long as the bottom surface of the inclusion was within the depth sensitivity of the PEF with the largest probe size. This work offers a simple non-invasive method to predict the extent of a tumor in all 3 dimensions. The method is also non-radioactive.

Factors affecting the concordance of radiologic and pathologic tumor size in breast carcinoma

Background

Radiologic assessment of tumor size is an integral part of the work-up for breast carcinoma. With improved radiologic equipment, surgical decision relies profoundly upon radiologic/clinical stage. We wanted to see the concordance between radiologic and pathologic tumor size to infer how accurate radiologic/clinical staging is.

Materials and methods

The surgical pathology and ultrasonography reports of patients with breast carcinoma were reviewed. Data were collected for 406 cases. Concordance was defined as a size difference within ±2 mm.

Results

The difference between radiologic and pathologic tumor size was within ±2 mm in 40.4% cases. The mean radiologic size was 1.73 ± 1.06 cm. The mean pathologic size was 1.84 ± 1.24 cm. A paired t-test showed a significant mean difference between radiologic and pathologic measurements (0.12 ± 1.03 cm, p = 0.03). Despite the size difference, stage classification was the same in 59.9% of cases. Radiologic size overestimated stage in 14.5% of cases and underestimated stage in 25.6% of cases. The concordance rate was significantly higher for tumors ≤2 cm (pT1) (51.1%) as compared to those greater than 2 cm (≥pT2) (19.7%) (p < 0.0001). Significantly more lumpectomy specimens (47.5%) had concordance when compared to mastectomy specimens (29.8%) (p < 0.0001). Invasive ductal carcinoma had better concordance compared to other tumors (p = 0.02).

Conclusion

Mean pathologic tumor size was significantly different from mean radiologic tumor size. Concordance was in just over 40% of cases and the stage classification was the same in about 60% of cases only. Therefore, surgical decision of lumpectomy versus mastectomy based on radiologic tumor size may not always be accurate.

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, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial

OBJECTIVE

The objective of our study was to determine the accuracy of preoperative measurements for detecting pathologic complete response (CR) and assessing residual disease after neoadjuvant chemotherapy (NACT) in patients with locally advanced breast cancer.

SUBJECTS AND METHODS

The American College of Radiology Imaging Network 6657 Trial prospectively enrolled women with ≥ 3 cm invasive breast cancer receiving NACT. Preoperative measurements of residual disease included longest diameter by mammography, MRI, and clinical examination and functional volume on MRI. The accuracy of preoperative measurements for detecting pathologic CR and the association with final pathology size were assessed for all lesions, separately for single masses and nonmass enhancements (NMEs), multiple masses, and lesions without ductal carcinoma in situ (DCIS).

RESULTS

In the 138 women with all four preoperative measures, longest diameter by MRI showed the highest accuracy for detecting pathologic CR for all lesions and NME (AUC = 0.76 and 0.84, respectively). There was little difference across preoperative measurements in the accuracy of detecting pathologic CR for single masses (AUC = 0.69-0.72). Longest diameter by MRI and longest diameter by clinical examination showed moderate ability for detecting pathologic CR for multiple masses (AUC = 0.78 and 0.74), and longest diameter by MRI and longest diameter by mammography showed moderate ability for detecting pathologic CR for tumors without DCIS (AUC = 0.74 and 0.71). In subjects with residual disease, longest diameter by MRI exhibited the strongest association with pathology size for all lesions and single masses (r = 0.33 and 0.47). Associations between preoperative measures and pathology results were not significantly influenced by tumor subtype or mammographic density.

CONCLUSION

Our results indicate that measurement of longest diameter by MRI is more accurate than by mammography and clinical examination for preoperative assessment of tumor residua after NACT and may improve surgical planning.

Tumor Size in Breast Carcinoma: Gross Measurement Is Important!

Introduction. The staging of breast carcinoma is mainly dependent on tumor size and lymph node status. Small increments in tumor size upstage the patient. An accurate determination of the tumor size is therefore critically important. Although the final staging is based on microscopic size, pathologists rely on gross measurements in a considerable number of cases. Methods. We investigated the concordance between gross and microscopic measurements of breast carcinoma as well as factors affecting this concordance. This study is a retrospective review of surgical pathology reports of invasive breast carcinomas. Data were collected for 411 cases. Concordance was defined as a size difference within ±2 mm. Results. Gross and microscopic sizes were identical in 33.1% of cases. Gross and microscopic size difference was within ±2 mm in 56% of cases. Despite the size difference, stage classification ended up being the same in 68.6% of cases. Tumor stage was over estimated by gross measurement in 17.0% of cases and underestimated in 14.4% of cases. The concordance was significantly higher for those tumors in which final pathologic tumor (pT) size was greater than 2 cm (≥pT2) as compared with those less than or equal to 2 cm (≤pT1; P < .0001). A higher proportion of mastectomy specimens (61.4%) were concordant as compared with lumpectomy specimens (52.1%). Conclusion. Gross and microscopic tumor sizes were concordant in 56% of cases. Stage classification based on gross and microscopic tumor size was different in nearly one third (31.4%) of cases. Gross tumor size is critically important in accurate staging at least in cases where tumor size cannot be confirmed microscopically.

Residual microcalcifications after neoadjuvant chemotherapy for locally advanced breast cancer: comparison of the accuracies of mammography and MRI in predicting pathological residual tumor

Background

The aims of this study were to correlate residual mammographic microcalcifications after neoadjuvant chemotherapy (NAC) with pathological results and to compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors.

Methods

The imaging findings and pathological results for 29 patients with residual microcalcifications after NAC were reviewed. We compared the agreement of the measured extent of residual microcalcifications based on MG and residual enhancement based on MRI with the residual tumor size based on pathology.

Results

At final pathology, residual microcalcifications were malignant in 55.2% of cases and benign in 44.8% of cases. In 36% of non-pCR cases, the remaining microcalcifications were benign. Compared with the measurements of residual tumor obtained from pathology, MG showed poor agreement, and MRI showed moderate agreement, for the entire group (concordance correlation coefficient [CCC] = 0.196 vs. 0.566). Regarding the receptor status, the agreement of measurements obtained by MG was superior to that obtained by MRI (CCC = 0.5629, 0.5472 vs. 0.4496, 0.4279) for ER(+) and HER2(−) tumors. In ER(−) tumors, the measurements obtained by MG showed the lowest agreement with the pathological tumor size, which had the highest agreement with those obtained by MRI (CCC = − 0.0162 vs. 0.8584).

Conclusions

Residual mammographic microcalcifications after NAC did not correlate with malignancy in 44.8% of cases. Residual microcalcifications on MG were poorly correlated with pathological tumor size, and MRI might be more reliable for predicting residual tumor size after NAC. Tumor receptor status affected the accuracy of both MG and MRI for predicting residual tumor size after NAC.

Trial registration

CRIS, KCT0002281; registered 6 April 2015, retrospectively registered

Thermal tomography for monitoring tumor response to neoadjuvant chemotherapy in women with locally advanced breast cancer

BACKGROUND & AIMS

This study aims to analyze the feasibility and predictive value of thermal tomography (TT) for monitoring early treatment response in patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC).

METHODS

Patients with LABC who were due to receive six cycles of NAC were examined by TT prior to NAC, the second cycle of NAC, the fourth cycle of NAC and surgery. Changes in TT parameters and ultrasonography were correlated with pathologic response to NAC, and the predictive value was assessed.

RESULTS

Forty-four patients were evaluable for response (25 pathologic responders and 19 nonresponders). As early as after the first cycle of NAC, changes in the TT parameters ΔTs, ΔTn, and ΔTa correlated significantly with pathologic response (P < 0.05). The best predictor of pathologic response after the 6th cycle of NAC was TT (area under the receiver operating characteristic curve, 0.794), as opposed to cross-sectional areas and the longest diameter by ultrasonography.

CONCLUSIONS

TT allows for monitoring early tumor response to NAC and can predict pathologic response in the early stages of therapy. Therefore, TT could be used as a novel imaging modality to monitor NAC treatment.

MRI and Prediction of Pathologic Complete Response in the Breast and Axilla after Neoadjuvant Chemotherapy for Breast Cancer

BACKGROUND

In the setting where determining extent of residual disease is key for surgical planning after neoadjuvant chemotherapy (NAC), we evaluate the reliability of MRI in predicting pathologic complete response (pCR) of the breast primary and axillary nodes after NAC.

STUDY DESIGN

Patients who had MRI before and after NAC between June 2014 and August 2015 were identified in a prospective database after IRB approval. Post-NAC MRI of the breast and axillary nodes was correlated with residual disease on final pathology. Pathologic complete response was defined as absence of invasive and in situ disease.

RESULTS

We analyzed 129 breast cancers. Median patient age was 50.8 years (range 27.2 to 80.6 years). Tumors were human epidermal growth factor receptor 2 amplified in 52 of 129 (40%), estrogen receptor-positive/human epidermal growth factor receptor 2-negative in 45 of 129 (35%), and triple negative in 32 of 129 (25%), with respective pCR rates of 50%, 9%, and 31%. Median tumor size pre- and post-NAC MRI were 4.1 cm and 1.45 cm, respectively. Magnetic resonance imaging had a positive predictive value of 63.4% (26 of 41) and negative predictive value of 84.1% (74 of 88) for in-breast pCR. Axillary nodes were abnormal on pre-NAC MRI in 97 patients; 65 had biopsy-confirmed metastases. The nodes normalized on post-NAC MRI in 33 of 65 (51%); axillary pCR was present in 22 of 33 (67%). In 32 patients with proven nodal metastases and abnormal nodes on post-NAC MRI, 11 achieved axillary pCR. In 32 patients with normal nodes on pre- and post-NAC MRI, 6 (19%) had metastasis on final pathology.

CONCLUSIONS

Radiologic complete response by MRI does not predict pCR with adequate accuracy to replace pathologic evaluation of the breast tumor and axillary nodes.

Development of array piezoelectric fingers towards in vivo breast tumor detection

We have investigated the development of a handheld 4 × 1 piezoelectric finger (PEF) array breast tumor detector system towards in vivo patient testing, particularly, on how the duration of the DC applied voltage, the depression depth of the handheld unit, and breast density affect the PEF detection sensitivity on 40 patients. The tests were blinded and carried out in four phases: with DC voltage durations 5, 3, 2, to 0.8 s corresponding to scanning a quadrant, a half, a whole breast, and both breasts within 30 min, respectively. The results showed that PEF detection sensitivity was unaffected by shortening the applied voltage duration from 5 to 0.8 s nor was it affected by increasing the depression depth from 2 to 6 mm. Over the 40 patients, PEF detected 46 of the 48 lesions (46/48)-with the smallest lesion detected being 5 mm in size. Of 28 patients (some have more than one lesion) with mammography records, PEF detected 31/33 of all lesions (94%) and 14/15 of malignant lesions (93%), while mammography detected 30/33 of all lesions (91%) and 12/15 of malignant lesions (80%), indicating that PEF could detect malignant lesions not detectable by mammography without significantly increasing false positives. PEF's detection sensitivity is also shown to be independent of breast density, suggesting that PEF could be a potential tool for detecting breast cancer in young women and women with dense breasts.

An Engineered Breast Cancer Model on a Chip to Replicate ECM-Activation In Vitro during Tumor Progression

In this work, a new model of breast cancer is proposed featuring both epithelial and stromal tissues arranged on a microfluidic chip. The main task of the work is the in vitro replication of the stromal activation during tumor epithelial invasion. The activation of tumor stroma and its morphological/compositional changes play a key role in tumor progression. Despite emerging evidences, to date the activation of tumor stroma in vitro has not been achieved yet. The tumor-on-chip proposed in this work is built in order to replicate the features of its native counterpart: multicellularity (tumor epithelial cell and stromal cell); 3D engineered stroma compartment composed of cell-assembled extracellular matrix (ECM); reliable 3D tumor architecture. During tumor epithelial invasion the stroma displayed an activation process at both cellular and ECM level. Similarly of what repeated in vivo, ECM remodeling is found in terms of hyaluronic acid and fibronectin overexpression in the stroma compartment. Furthermore, the cell-assembled ECM featuring the stromal tissue, allowed on-line monitoring of collagen remodeling during stroma activation process via real time multiphoton microscopy. Also, trafficking of macromolecules within the stromal compartment has been monitored in real time.

Multiparametric MR Imaging of Breast Cancer

Breast MR imaging has increased in popularity over the past 2 decades due to evidence of its high sensitivity for cancer detection. Current clinical MR imaging approaches rely on the use of a dynamic contrast-enhanced acquisition that facilitates morphologic and semiquantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters holds promise to broaden the utility of MR imaging and improve its specificity. Because of wide variations in approaches for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use are not yet available, limiting current applications of many of these tools to research purposes.

Assessment of Residual Disease With Molecular Breast Imaging in Patients Undergoing Neoadjuvant Therapy: Association With Molecular Subtypes

BACKGROUND

Assessment of residual disease after neoadjuvant therapy for breast cancer is an ongoing challenge of breast imaging. This study evaluates the accuracy of a novel dedicated system for molecular breast imaging (MBI) composed of the new generation of cadmium zinc telluride detectors in assessing residual disease after neoadjuvant therapy in patients with breast cancer.

PATIENTS AND METHODS

Clinical data, imaging, surgical, and pathological findings of 51 women with breast cancer undergoing neoadjuvant therapy were recorded. MBI findings were correlated with surgical pathology results. Accuracy of MBI in predicting complete pathological response and size of residual disease was assessed according to molecular subtypes.

RESULTS

The size of the largest focus of uptake on MBI correlated with the largest dimension measured on pathology (r = 0.55; P < .001). This correlation was stronger for triple negative and HER2/neu positive subtypes (r = 0.92 and 0.62, respectively). Sixteen patients (31%) had complete pathological response. The sensitivity and specificity of MBI for detecting residual disease were 83% (95% confidence interval [CI], 66-93) and 69% (95% CI, 42-88), respectively. For triple negative or HER2/neu positive disease the sensitivity and specificity were 88% (95% CI, 62-98) and 75% (95% CI, 43-93), respectively.

CONCLUSION

The accuracy of MBI in assessing residual disease after neoadjuvant treatment might be related to the molecular subtype. Accuracy is highest in the triple negative and HER2/neu positive subtypes.

Serial MRI scans help in assessing early response to neoadjuvant chemotherapy and tailoring breast cancer treatment

BACKGROUND

Tailoring neoadjuvant chemotherapy (NAC) during breast cancer treatment is performed to improve overall tumour response, with increasing evidence to support its role. This study evaluates our breast unit's experience in MRI assessment of tumour response as an aid in tailoring NAC.

MATERIALS AND METHODS

This is a retrospective study of patients treated with NAC for breast cancer between 2005 and 2009 who underwent MRI to assess tumour response. Response to NAC was monitored before NAC and after 2 and/or 4 cycles of anthracycline and cyclophosphamide (AC) chemotherapy. Taxane was substituted for AC if MRI response was deemed inadequate. Tumour response on last MRI was correlated with final pathology against different tumour subtypes and in inflammatory tumours. Strength of agreement was measured using Kappa analysis. Potential predictive factors for MRI response were assessed for significance.

RESULTS

166 tumours were assessed with serial MRI scans. MRI showed high sensitivity rate (93.1%) in predicting response to NAC particularly for tumours showing partial (PR) or complete (CR) response on pathology (p < 0.001) with fair agreement on Kappa analysis (K = 0.31). MRI seems more accurate in triple negative, HR+/HER2+ and high-grade tumours. Early identification of non-responders on MRI resulted in early tailoring of NAC, with improved rates of tumour response seen in 74.2% following switching NAC. Logistic regression showed that PR or CR observed on MRI after 2 NAC cycles significantly predicted pCR (p < 0.001).

CONCLUSION

Serial MRI can be used to assess patterns of tumour response to NAC. This study shows that tailoring NAC according to pattern of response can improve overall tumour response rates.

Does change in microcalcifications with neoadjuvant treatment correlate with pathological tumour response?

AIM

To determine whether change in microcalcification density and extent after neoadjuvant treatment (NAT) can predict tumour response.

MATERIALS AND METHODS

This single-institution, retrospective study included all women with breast cancer who underwent NAT between 1 January 2008 and 31 December 2014, and fulfilled the following criteria: mammography before NAT with pathological microcalcifications, mammography performed after NAT, and tumour resection at Tel-Aviv Sourasky Medical Center. Correlation was made between mammography features and clinicopathological information.

RESULTS

Fifty-four patients met the inclusion criteria. Post-NAT, the number of calcifications remained stable in 30 (55.5%) patients, decreased in 23 (42.6%) patients, and increased in one (1.9%) patient. Patients with a decreased number of malignant calcifications post-NAT had higher rates of pathological complete response compared to patients with no change (59% versus 20%, p=0.009). Patients with triple negative and human epidermal growth factor receptor 2 (HER2) receptor subtypes had higher rates of decreased number of calcifications post-NAT (50% versus 35%) and pathological complete response (57% versus 11%, p=0.007) compared to patients with luminal receptor subtype. In addition, patients who received a combination of chemotherapy and biological treatment had more cases of decreased number of calcifications compared to patients who received chemotherapy alone (56% versus 39%). No significant correlation was observed between calcification change post-NAT and calcification morphology or distribution pattern.

CONCLUSIONS

Patients with breast carcinoma and decreased number of pathological calcifications post-NAT had higher rates of pathological complete response compared to patients with no change in calcifications; however, a substantial number of patients with complete pathological response had no change in microcalcification distribution with treatment, questioning the need to completely excise all calcifications post-NAT.

Ability of contrast-enhanced ultrasonography to determine clinical responses of breast cancer to neoadjuvant chemotherapy

OBJECTIVE

We aimed to determine whether contrast-enhanced ultrasonography can predict the effects of neoadjuvant chemotherapy on breast cancer.

METHODS

The clinical responses of 63 consecutive patients with breast cancer (T1-4, N0-1, M0) to neoadjuvant chemotherapy between October 2012 and May 2015 were assessed using contrast-enhanced magnetic resonance imaging, positron emission tomography/computed tomography and contrast-enhanced ultrasonography. Perfusion parameters for contrast-enhanced ultrasonography were created from time-intensity curves based on enhancement intensity and temporal changes to objectively evaluate contrast-enhanced ultrasonography findings. The sensitivity, specificity and accuracy of contrast-enhanced ultrasonography, magnetic resonance imaging and positron emission tomography/computed tomography to predict a pathological complete response were compared after confirming the pathological findings of surgical specimens.

RESULTS

Twenty-three (36.5%) of the 63 patients achieved pathological complete response. The sensitivity, specificity and accuracy of contrast-enhanced ultrasonography for predicting pathological complete response were 95.7% (82.5-99.2%), 77.5% (69.9-79.5%) and 84.1% (74.5-86.7%). The sensitivity of contrast-enhanced ultrasonography was significantly greater than that of magnetic resonance imaging (95.7 vs. 69.6%, P = 0.047). The specificity and accuracy were significantly greater and tended to be greater, respectively, for contrast-enhanced ultrasonography than positron emission tomography/computed tomography (specificity, 77.5 vs. 52.5%, P = 0.02; accuracy, 84.1 vs. 69.8%, P = 0.057).

CONCLUSIONS

Contrast-enhanced ultrasonography might serve as a new diagnostic modality when planning therapeutic strategies for patients with breast cancer after neoadjuvant chemotherapy.

Breast tumor detection using UWB circular-SAR tomographic microwave imaging

This paper describes the possibility of detecting tumors in human breast using ultra-wideband (UWB) circular synthetic aperture radar (CSAR). CSAR is a subset of SAR which is a radar imaging technique using a circular data acquisition pattern. Tomographic image reconstruction is done using a time domain global back projection technique adapted to CSAR. Experiments are conducted on a breast phantoms made of pork fat emulating normal and cancerous conditions. Preliminary experimental results show that microwave imaging of a breast phantom using UWB-CSAR is a simple and low-cost method, efficiently capable of detecting the presence of tumors.

Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL

PURPOSE

To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response (PCR).

MATERIALS AND METHODS

This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrast-enhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (ΔFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics.

RESULTS

Female patients (n = 162) with FTV and RFS were included. At univariate analysis, FTV2, FTV4, and ΔFTV4 had significant association with RFS, as did HR/HER2 status and RCB class. PCR approached significance at univariate analysis and was not significant at multivariate analysis. At univariate analysis, FTV2 and RCB class had the strongest predictive performance (C statistic = 0.67; 95% confidence interval [CI]: 0.58, 0.76), greater than for FTV4 (0.64; 95% CI: 0.53, 0.74) and PCR (0.57; 95% CI: 0.39, 0.74). At multivariate analysis, a model with FTV2, ΔFTV2, RCB class, HR/HER2 status, age, and race had the highest C statistic (0.72; 95% CI: 0.60, 0.84).

CONCLUSION

Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.

Systemic treatment in breast cancer: a primer for radiologists

Abstract

Cytotoxic chemotherapy, hormonal therapy and molecular targeted therapy are the three major classes of drugs used to treat breast cancer. Imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), ¹⁸F-FDG positron emission tomography (PET)/CT and bone scintigraphy each have a distinct role in monitoring response and detecting drug toxicities associated with these treatments. The purpose of this article is to elucidate the various systemic therapies used in breast cancer, with an emphasis on the role of imaging in assessing treatment response and detecting treatment-related toxicities.

Teaching Points

• Cytotoxic chemotherapy is often used in combination with HER2-targeted and endocrine therapies.

• Endocrine and HER2-targeted therapies are recommended in hormone-receptor- and HER2-positive cases.

• CT is the workhorse for assessment of treatment response in breast cancer metastases.

• Alternate treatment response criteria can help in interpreting pseudoprogression in metastasis.

• Unique toxicities are associated with cytotoxic chemotherapy and with endocrine and HER2-targeted therapies.

Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis

Background

Magnetic resonance imaging (MRI) may guide breast cancer surgery by measuring residual tumor size post-neoadjuvant chemotherapy (NAC). Accurate measurement may avoid overly radical surgery or reduce the need for repeat surgery. This individual patient data (IPD) meta-analysis examines MRI’s agreement with pathology in measuring the longest tumor diameter and compares MRI with alternative tests.

Methods

A systematic review of MEDLINE, EMBASE, PREMEDLINE, Database of Abstracts of Reviews of Effects, Heath Technology Assessment, and Cochrane databases identified eligible studies. Primary study authors supplied IPD in a template format constructed a priori. Mean differences (MDs) between tests and pathology (i.e. systematic bias) were calculated and pooled by the inverse variance method; limits of agreement (LOA) were estimated. Test measurements of 0.0 cm in the presence of pathologic residual tumor, and measurements >0.0 cm despite pathologic complete response (pCR) were described for MRI and alternative tests.

Results

Eight studies contributed IPD (N = 300). The pooled MD for MRI was 0.0 cm (LOA: +/−3.8 cm). Ultrasound underestimated pathologic size (MD: −0.3 cm) relative to MRI (MD: 0.1 cm), with comparable LOA. MDs were similar for MRI (0.1 cm) and mammography (0.0 cm), with wider LOA for mammography. Clinical examination underestimated size (MD: −0.8 cm) relative to MRI (MD: 0.0 cm), with wider LOA. Tumors “missed” by MRI typically measured 2.0 cm or less at pathology; tumors >2.0 cm were more commonly “missed” by clinical examination (9.3 %). MRI measurements >5.0 cm occurred in 5.3 % of patients with pCR, but were more frequent for mammography (46.2 %).

Conclusions

There was no systematic bias in MRI tumor measurement, but LOA are large enough to be clinically important. MRI’s performance was generally superior to ultrasound, mammography, and clinical examination, and it may be considered the most appropriate test in this setting. Test combinations should be explored in future studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1664-4) contains supplementary material, which is available to authorized users.

Prognostic Significance of a Complete Response on Breast MRI in Patients Who Received Neoadjuvant Chemotherapy According to the Molecular Subtype

OBJECTIVE

To evaluate the relationship between response categories assessed by magnetic resonance imaging (MRI) or pathology and survival outcomes, and to determine whether there are prognostic differences among molecular subtypes.

MATERIALS AND METHODS

We evaluated 174 patients with biopsy-confirmed invasive breast cancer who had undergone MRI before and after neoadjuvant chemotherapy, but before surgery. Pathology findings were classified as a pathologic complete response (pCR) or a non-pCR, and MRI findings were designated as a radiologic CR (rCR) or a non-rCR. We evaluated overall and subtype-specific associations between clinicopathological factors including the assessment categories and recurrence, using the Cox proportional hazards model.

RESULTS

There were 41 recurrences (9 locoregional and 32 distant recurrences). There were statistically significant differences in recurrence outcomes between patients who achieved a radiologic or a pCR and patients who did not achieve a radiologic or a pCR (recurrence hazard ratio, 11.02; p = 0.018 and recurrence hazard ratio, 3.93; p = 0.022, respectively). Kaplan-Meier curves for recurrence-free survival showed that triple-negative breast cancer was the only subtype that showed significantly better outcomes in patients who achieved a CR compared to patients who did not achieve a CR by both radiologic and pathologic assessments (p = 0.004 and 0.001, respectively). A multivariate analysis found that patients who achieved a rCR and a pCR did not display significantly different recurrence outcomes (recurrence hazard ratio, 2.02; p = 0.505 and recurrence hazard ratio, 1.12; p = 0.869, respectively).

CONCLUSION

Outcomes of patients who achieved a rCR were similar to those of patients who achieved a pCR. To evaluate survival difference according to molecular subtypes, a larger study is needed.

Effective factors to raise diagnostic performance of breast MRI for diagnosing pathologic complete response in breast cancer patients after neoadjuvant chemotherapy

BACKGROUND

Although MRI is a highly effective tool in evaluating residual disease after neoadjuvant chemotherapy (NAC), there are many reports of discordance between the response of MRI and pathology. To increase MR accuracy, additional methods, which reflect post-NAC changes, should be considered in diagnosis.

PURPOSE

To evaluate effective methods that raise the diagnostic performance of MRI for predicting pathologic complete response (pCR) in breast cancer after neoadjuvant chemotherapy (NAC).

MATERIAL AND METHODS

For 98 invasive breast carcinoma patients, chemotherapeutic response to MRI was evaluated for the following parameters: tumor size, tumor distribution pattern, kinetic curve analysis, and background parenchymal enhancement pattern (BPE). BPE was categorized as "minimal", "mild", "moderate", or "marked", according to the ACR BI-RADS criteria.

RESULTS

After NAC, the mean size of tumors decreased by 40% in non-pCR and by 59% in pCR groups, respectively. The sensitivity, specificity, false positive rate and false negative rate of MRI were 96% (78/81), 53% (9/17), 47% (8/17), and 4% (3/81), respectively. At pre-NAC MRI, the most common kinetic curve was delayed washout pattern (68%, 67/98); however, at post-NAC MRI the persistent pattern (55%, 47/86). Grouped lesion was the most common tumor distribution pattern on pre-NAC MRI (28%, 27/98), while on post-NAC solitary mass (40%, 34/86). The most common BPE at pre- and post-NAC MRI was mild and minimal enhancement, respectively.

CONCLUSION

To improve the diagnostic accuracy of MRI, we should consider additional factors including: tumor distribution pattern, BPE, kinetic curve analysis, and tumor size.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach.

Breast magnetic resonance imaging indications

The use of breast magnetic resonance imaging (MRI) has grown for the past decade and, along with the increase in use, there has been a progression in the indications for use. Breast MRI has been shown to be a valuable additional tool for breast imagers to use to provide optimal patient care. Because of the benefit that MRI can provide, the technology is now being used for a wide variety of indications, from evaluation of the extent of disease to evaluation of the high-risk patient, evaluation of tumor response to chemotherapy, and search for occult primary tumor. This review will cover the various indications for breast MRI, discuss research to date, as well as provide case examples.

Surgical considerations in locally advanced breast cancer patients receiving neoadjuvant chemotherapy

PURPOSE OF REVIEW

The definition of locally advanced breast cancer (LABC) includes patients with large tumors, extensive regional lymph node involvement, or direct involvement of the skin or underlying chest wall. Neoadjuvant chemotherapy followed by surgery has become the standard of care and a valuable strategy in the multimodality management of LABC. Variations in practice exist and the purpose of this article is to explore the surgical considerations in the management of LABC.

RECENT FINDINGS

There exist various diagnostic and treatment considerations in LABC patients that help guiding clinicians in the optimal management of LABC. The evolving concepts of breast conservation, immediate breast reconstruction and optimal management of the axilla are addressed.

SUMMARY

LABC represents a heterogenous cohort of patients for whom a multidisciplinary care team is critical. A more detailed understanding of the surgical considerations will facilitate the optimal diagnostic evaluation and management of these patients.

Prediction of pathological complete response of breast cancer patients undergoing neoadjuvant chemotherapy: usefulness of breast MRI computer-aided detection

OBJECTIVE

To evaluate the usefulness of MR computer-aided detection (CAD) in patients undergoing neoadjuvant chemotherapy for prediction of the pathological complete response of tumours.

METHODS

148 patients with breast cancer (mean age, 47.3 years; range, 29-72 years) who underwent neoadjuvant chemotherapy were included in our study. They underwent MRI before and after neoadjuvant chemotherapy, and we reviewed the pathological result as the gold standard. The computer-generated kinetic features for each lesion were recorded, and the features analysed included "threshold enhancement" at 50% and 100% minimum thresholds; degree of initial peak enhancement; and enhancement profiles comprising lesion percentages of washout, plateau and persistent enhancement. The final pathological size and character of tumours were correlated with post-chemotherapy mammography, ultrasonography and MR CAD findings. Kruskal-Wallis test and intraclass correlation coefficient were used to analyse the findings.

RESULTS

We divided the 148 patients into complete pathological response and non-complete pathological response groups. A complete pathological response was defined as no histopathological evidence of any residual invasive cancer cells in the breast or axillary lymph nodes. 39 patients showed complete pathological response, and 109 patients showed non-complete pathological response. Between enhancement profiles of MR CAD, plateau proportion of tumours was significantly correlated with the pathological response of tumours (mean proportion of plateau on complete pathological response group was 27%, p = 0.007).

CONCLUSION

When plateau proportion of tumours is high, we can predict non-complete pathological response of neoadjuvant chemotherapy.

ADVANCES IN KNOWLEDGE

MR CAD can be a useful tool for the assessment of response to neoadjuvant chemotherapy and prediction of pathological results.

Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy

PURPOSE

To evaluate whether changes in magnetic resonance (MR) imaging heterogeneity may aid assessment for pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in primary breast cancer and to compare pCR with standard Response Evaluation Criteria in Solid Tumors response.

MATERIALS AND METHODS

Institutional review board approval, with waiver of informed consent, was obtained for this retrospective analysis of 36 consecutive female patients, with unilateral unifocal primary breast cancer larger than 2 cm in diameter who were receiving sequential anthracycline-taxane NACT between October 2008 and October 2012. T2- and T1-weighted dynamic contrast material-enhanced MR imaging was performed before, at midtreatment (after three cycles), and after NACT. Changes in tumor entropy (irregularity) and uniformity (gray-level distribution) were determined before and after MR image filtration (for different-sized features). Entropy and uniformity for pathologic complete responders and nonresponders were compared by using the Mann-Whitney U test and receiver operating characteristic analysis.

RESULTS

With NACT, there was an increase in uniformity and a decrease in entropy on T2-weighted and contrast-enhanced subtracted T1-weighted MR images for all filters (uniformity: 23.45% and 22.62%; entropy: -19.15% and -19.26%, respectively). There were eight complete pathologic responders. An area under the curve of 0.84 for T2-weighted MR imaging entropy and uniformity (P = .004 and .003) and 0.66 for size (P = .183) for pCR was found, giving a sensitivity and specificity of 87.5% and 82.1% for entropy and 87.5% and 78.6% for uniformity compared with 50% and 82.1%, respectively, for tumor size change for association with pCR.

CONCLUSION

Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tumor size change.

Utility of MRI and PET/CT after neoadjuvant chemotherapy in breast cancer patients: correlation with pathological response grading system based on tumor cellularity

BACKGROUND

MRI and PET/CT are useful for assessing breast cancer patients after neoadjuvant chemotherapy (NAC).

PURPOSE

To investigate the utility of MRI and PET/CT in the prediction of pathologic response to neoadjuvant chemotherapy using Miller-Payne grading system in patients with breast cancer.

MATERIAL AND METHODS

From January 2008 to December 2010, 59 consecutive patients with pathologically proven breast cancer, who underwent neoadjuvant chemotherapy followed by surgery were retrospectively enrolled. The maximal diameter decrease rate and volume reduction rate by three-dimensional (3D) MRI and standardized uptake value (SUV) reduction rate by PET/CT were calculated and correlated with the Miller-Payne grading system using the Spearman rank correlation test. Patients with Miller-Payne grades 1 or 2 were classified into the non-responder group and patients with grades 3, 4, and 5 were in the responder group. To differentiate between responders and non-responders, receiver-operating characteristic (ROC) analysis was performed.

RESULTS

The volume reduction rate was 64.87 ± 46.95, diameter decrease rate was 48.09 ± 35.02, and SUV decrease rate was 62.10 ± 32.17. Among three parameters, the volume reduction rate was most correlated with histopathologic grades of regression (ρ = 0.755, P < .0001) followed by diameter decrease rate (ρ = 0.660, P < 0.0001), and SUV decrease rate of primary breast mass (ρ = 0.561, P = 0.0002). The area under the ROC curve (Az) value was largest in the volume reduction rate (Az = 0.9), followed by SUV decrease rate (Az = 0.875), and diameter decrease rate (Az = 0.849). The best cut-offs for differentiating responders from non-responders in the ROC curve analysis were a 50% decrease in diameter, 68.9% decrease in volume, and 60.1% decrease in SUV after NAC.

CONCLUSION

Volumetric measurement using 3D MRI combined with conventional diameter measurement may be more accurate to evaluate pathologic response after NAC.

The role of mammographic calcification in the neoadjuvant therapy of breast cancer imaging evaluation

Introduction

Investigate the patterns of mammographically detected calcifications before and after neoadjuvant chemotherapy (NACT) to determine their value for efficacy evaluation and surgical decision making.

Methods

187 patients with malignant mammographic calcifications were followed to record the appearances and changes in the calcifications and to analyze their responses to NACT.

Results

Patients with calcifications had higher rates of hormonal receptor (HR) positive tumors (74.3% versus 64.6%) and HER2 positive tumors (51.3% versus 33.4%, p = 0.004) and a similar pathologic complete response (pCR) rate compared to patients without calcifications (35.4% versus 29.8%). After NACT, the range of calcification decreased in 40% of patients, increased in 7.5% and remained stable in 52.5%; the calcification density decreased in 15% of patients, increased in 7.5% and remained stable in 77.5%; none of these change patterns were related to tumor response rate. No significant correlation was observed between the calcification appearance (morphology, distribution, range, diameter or density) and tumor subtypes or pCR rates. Among patients with malignant calcifications, 54 showed calcifications alone, 40 occurred with an architectural distortion (AD) and 93 with a mass. Calcifications were observed inside the tumor in 44% of patients and outside in 56%, with similar pCR rates and patterns of change.

Conclusions

Calcification appearance did not clearly change after NACT, and calcification patterns were not related to pCR rate, suggesting that mammogram may not accurate to evaluate tumor response changes. Microcalcifications visible after NACT is essential for determining the extent of excision, patients with calcifications that occurred outside of the mass still had the opportunity for breast conservation.

Meta-analysis of agreement between MRI and pathologic breast tumour size after neoadjuvant chemotherapy

BACKGROUND

Magnetic resonance imaging (MRI) has been proposed to guide breast cancer surgery by measuring residual tumour after neoadjuvant chemotherapy. This study-level meta-analysis examines MRI's agreement with pathology, compares MRI with alternative tests and investigates consistency between different measures of agreement.

METHODS

A systematic literature search was undertaken. Mean differences (MDs) in tumour size between MRI or comparator tests and pathology were pooled by assuming a fixed effect. Limits of agreement (LOA) were estimated from a pooled variance by assuming equal variance of the differences across studies.

RESULTS

Data were extracted from 19 studies (958 patients). The pooled MD between MRI and pathology from six studies was 0.1 cm (95% LOA: -4.2 to 4.4 cm). Similar overestimation for MRI (MD: 0.1 cm) and ultrasound (US) (MD: 0.1 cm) was observed, with comparable LOA (two studies). Overestimation was lower for MRI (MD: 0.1 cm) than mammography (MD: 0.4 cm; two studies). Overestimation by MRI (MD: 0.1 cm) was smaller than underestimation by clinical examination (MD: -0.3 cm). The LOA for mammography and clinical examination were wider than that for MRI. Percentage agreement between MRI and pathology was greater than that of comparator tests (six studies). The range of Pearson's/Spearman's correlations was wide (0.21-0.92; 16 studies). Inconsistencies between MDs, percentage agreement and correlations were common.

CONCLUSION

Magnetic resonance imaging appears to slightly overestimate pathologic size, but measurement errors may be large enough to be clinically significant. Comparable performance by US was observed, but agreement with pathology was poorer for mammography and clinical examination. Percentage agreement can provide supplementary information to MDs and LOA, but Pearson's/Spearman's correlation does not provide evidence of agreement and should be avoided. Further comparisons of MRI and other tests using the recommended methods are warranted.

Circulating tumor cells as predictors of response and failure in breast cancer patients treated with preoperative chemotherapy

AIM

To explore the significance of circulating tumor cells (CTCs) detection in the course of preoperative chemotherapy (PC) and their effect on the outcomes.


METHODS

Fifty-five patients with stage II/III invasive breast cancer were enrolled into a preoperative clinical trial. Patients were given PC with sequential single-agent doxorubicin and paclitaxel vs paclitaxel followed by doxorubicin. Blood samples (8 mL) were collected from patients before PC, after each phase, and at 6 months intervals during follow-up. Peripheral blood mononuclear cells were isolated and enriched for epithelial cells. Quantitative RT-PCR was used to determine the presence of cytokeratin 19 (CK19) mRNA. Samples were considered positive when the PCR curve crossed the standard threshold curve.


RESULTS

After the first phase of chemotherapy, there was a 59% overall reduction in the median tumor volume. The percentage of volume reduction did not differ between patients who presented with detectable CTCs at baseline and those who did not (p=0.89). After the second phase of chemotherapy, there was a further decrease in the median tumor volume to 93% from baseline. There was no correlation between the lack of response and the presence of CTCs either after the first (p=0.36) or second (p=0.5391) phases of PC. The presence of CTCs was a predictor of local or distant relapse (p=0.0411). The detection of CTCs did not affect overall survival (p=0.2569).


CONCLUSION

CTCs can be used as predictors of relapse after definitive treatment of locally advanced breast cancer; however, CTCs detection in peripheral blood during the course of PC does not implicate a particular pattern of response to treatment.

Analysis of factors that influence the accuracy of magnetic resonance imaging for predicting response after neoadjuvant chemotherapy in locally advanced breast cancer

PURPOSE

The purpose of this study was to evaluate the accuracy of breast magnetic resonance imaging (MRI) to predict residual lesion size after neoadjuvant chemotherapy (NAC) and to determine the factors that influence the accuracy of response prediction.

METHODS

This study comprised 166 patients who underwent MRI before and after NAC, but before surgery. The longest diameter of the residual cancer was measured using MRI and correlated with pathologic findings. Patients were further divided into subgroups according to various radiologic and histopathologic factors. Pathologic complete response (pCR) was defined as the absence of residual invasive cancer cells. The Pearson correlation was used to correlate tumor size as determined by MRI and pathology, and the Mann-Whitney U test and Kruskal-Wallis test were used to compare MRI-pathologic size discrepancies according to various clinical, histopathologic factors, and MRI findings.

RESULTS

Of the 166 women, 40 achieved pCR. The overall sensitivity, specificity, and accuracy for diagnosing invasive residual disease by using MRI were 96, 65, and 89 %, respectively. The Pearson's correlation coefficient between the tumor sizes measured using MRI and pathology was 0.749 (P < 0.001). The size discrepancy was significantly greater in patients with estrogen receptor-positive cancer (P = 0.037), in cancers with low nuclear grade (P = 0.007), and in cancers shown as diffuse non-mass-like enhancement on MRI (P = 0.001).

CONCLUSIONS

Size prediction is less accurate in cases with estrogen receptor-positive breast cancer, low nuclear grade, and diffuse non-mass-like enhancement on initial MRI.

3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction

RATIONALE AND OBJECTIVES

The goal of the study is to develop a technique to achieve accurate volumetric breast tissue segmentation using magnetic resonance imaging (MRI) data. This segmentation can be useful to aid in the diagnosis of breast cancers and to assess breast cancer risk based on breast density. Tissue segmentation is also essential for development of acoustic and thermal models used in magnetic resonance guided high-intensity focused ultrasound treatment of breast lesions.

MATERIALS AND METHODS

In addition to commonly used T1-, T2-, and proton density-weighted images, three-point Dixon water- and fat-only images were also included as part of the multiparametric inputs to a tissue segmentation algorithm using a hierarchical support vector machine (SVM). The effectiveness of a variety of preprocessing schemes was evaluated through two in vivo datasets. The performance of the hierarchical SVM was investigated and compared to the conventional classification algorithms-conventional SVM and fuzzy C-mean (FCM).

RESULTS

The need for co-registration, zero-filled interpolation, coil sensitivity correction, and optimal SNR reconstruction before the final stage classification was demonstrated. The overlap ratios of the hierarchical SVM, conventional SVM and FCM were 93.25%-94.08%, 81.68-92.28%, and 75.96%-91.02%, respectively. Classification outputs from in vivo experiments showed that the presented methodology is consistent and outperforms other algorithms.

CONCLUSION

The presented hierarchical SVM-based technique showed promising results in automatically segmenting breast tissues into fat, fibroglandular tissue, skin, and lesions. The results provide evidence that both the multiparametric breast MRI inputs and the preprocessing procedures contribute to the high accuracy of tissue classification.