Evaluation of neoadjuvant chemotherapy response in women with locally advanced breast cancer using ultrasound elastography

Influence of tumor microenvironment on the different breast cancer subtypes and applied therapies

Despite the significant improvements made in breast cancer therapy during the last decades, this disease still has increasing incidence and mortality rates. Different targets involved in general processes, like cell proliferation and survival, have become alternative therapeutic options for this disease, with some of them already used in clinic, like the CDK4/6 inhibitors for luminal A tumors treatment. Nevertheless, there is a demand for novel therapeutic strategies focused not only on tumor cells, but also on their microenvironment. Tumor microenvironment (TME) is a very complex and dynamic system that, more than surrounding and supporting tumor cells, actively participates in tumor development and progression. During the last decades, it has become clear that the cellular and acellular components of TME differ between the various breast cancer subtypes and shape the differences regarding their severity and prognosis. The pivotal role of the TME in controlling tumor growth and influencing responses to therapy represents a potential source for novel targets and therapeutic strategies. In this review, we present a description of the multiple therapeutic options used for different breast cancer subtypes, as well as the influence that the TME may exert on the development of the disease and on the response to the distinct therapies, which in some cases may explain their failure by the occurrence of relapses and resistance. Furthermore, the ongoing studies focused on the use of TME components for developing potential cancer treatments are described.

An overview of extracellular matrix and its remodeling in the development of cancer and metastasis with a glance at therapeutic approaches

The extracellular matrix (ECM) is an inevitable part of tissues able to provide structural support for cells depending on the purpose of tissues and organs. The dynamic characteristics of ECM let this system fluently interact with the extrinsic triggers and get stiffed, remodeled, and/or degraded ending in maintaining tissue homeostasis. ECM could serve as the platform for cancer progression. The dysregulation of biochemical and biomechanical ECM features might take participate in some pathological conditions such as aging, tissue destruction, fibrosis, and particularly cancer. Tumors can reprogram how ECM remodels by producing factors able to induce protein synthesis, matrix proteinase expression, degradation of the basement membrane, growth signals and proliferation, angiogenesis, and metastasis. Therefore, targeting the ECM components, their secretion, and their interactions with other cells or tumors could be a promising strategy in cancer therapies. The present study initially introduces the physiological functions of ECM and then discusses how tumor-dependent dysregulation of ECM could facilitate cancer progression and ends with reviewing the novel therapeutic strategies regarding ECM.

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies

BACKGROUND

Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival.

PURPOSE

This study, for the first time, proposes a hierarchical self-attention-guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre-treatment biopsy specimens.

METHODS

Digitized hematoxylin and eosin-stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional layers and transformer self-attention blocks were utilized in the patch-level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor-level processing and the patient-level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five-fold cross-validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework.

RESULTS

The obtained results on the test set showed an AUC of 0.89 and an F1-score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch-level, patch-level + tumor-level, and patch-level + patient-level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1-scores of 86%, 87%, and 89%, respectively.

CONCLUSIONS

The results demonstrate a high potential of the proposed hierarchical deep-learning methodology for analyzing digital pathology images of pre-treatment tumor biopsies to predict the pathological response of breast cancer to NAC.

Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

PURPOSE

An enhanced ultrasound elastography technique is proposed for early assessment of locally advanced breast cancer (LABC) response to neoadjuvant chemotherapy (NAC).

METHODS

The proposed elastography technique inputs ultrasound radiofrequency data obtained through tissue quasi-static stimulation and adapts a strain refinement algorithm formulated based on fundamental principles of continuum mechanics, coupled with an iterative inverse finite element method to reconstruct the breast Young's modulus (E) images. The technique was explored for therapy response assessment using data acquired from 25 LABC patients before and at weeks 1, 2, and 4 after the NAC initiation (100 scans). The E ratio of tumor to the surrounding tissue was calculated at different scans and compared to the baseline for each patient. Patients' response to NAC was determined many months later using standard clinical and histopathological criteria.

RESULTS

Reconstructed E ratio changes obtained as early as one week after the NAC onset demonstrate very good separation between the two cohorts of responders and non-responders to NAC. Statistically significant differences were observed in the E ratio changes between the two patient cohorts at weeks 1 to 4 after treatment (p-value < 0.001; statistical power greater than 97%). A significant difference in axial strain ratio changes was observed only at week 4 (p-value = 0.01; statistical power = 76%). No significant difference was observed in tumor size changes at weeks 1, 2 or 4.

CONCLUSION

The proposed elastography technique demonstrates a high potential for chemotherapy response monitoring in LABC patients and superior performance compared to strain imaging.

Diagnostic Performance of Strain and Shear Wave Elastography for the Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: Systematic Review and Meta-Analysis

OBJECTIVES

To investigate the diagnostic performance of strain and shear wave elastography for the response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

Relevant studies were searched in the databases of PubMed, Web of Science and Cochrane Library until October 2021. The diagnostic performance of ultrasonic elastography for the response to NAC were estimated by calculating the area under the curve (AUC) with sensitivity and specificity using Stata 14.0.

RESULTS

A total of 15 studies that comprise 1147 breast cancer patients were included in this meta-analysis. The pooled AUC of strain elastography in diagnosing responses were 0.89 (95% CI = 0.86-0.91) with 87% (95% CI = 75-94%) of sensitivity and 80% (95% CI = 72-84%) of specificity. The pooled AUC of shear wave elastography in diagnosing response were 0.82 (95% CI = 0.78-0.85) with 79% (95% CI = 72-84%) of sensitivity and 81% (95% CI = 71-88%). No publication bias was observed across the studies using Deek's funnel plot.

CONCLUSIONS

Based on current evidence, this meta-analysis confirmed that strain and shear wave elastography exhibited favorable performance for predicting responses to NAC. Strain and shear wave elastography may be a useful, noninvasive method for the assessment of response to NAC in breast cancer patients.

Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease’s therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial–mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the important role of the gene OGN in predicting the pCR rate. In conclusion, delineation of the ECM signature with immune-related genes is anticipated to aid in the prediction of the prognosis of patients with BC and the benefits of hormone therapy and NAC in BC patients.

Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies

Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.

Evaluation of change in tumor stiffness measured by acoustic radiation force impulse imaging for early prediction of response to neoadjuvant chemotherapy in breast cancer

OBJECTIVES

In this study, the role of change in tumor stiffness between pre- and post-two cycles of neoadjuvant chemotherapy (NACT) measured by Acoustic Radiation Force Impulse (ARFI) imaging for predicting the response to NACT in breast cancer was analyzed.

METHODS

Fifty-two adult women with biopsy-proven locally advanced breast cancer and early-stage breast cancer who received NACT followed by either modified radical mastectomy or breast conservation surgery were included in the study. Tumor stiffness represented by shear wave velocity (SWV) in meter per second by ARFI imaging was measured before and after two cycles of NACT. Participants were categorized into responders and nonresponders based on pathological response assessment from the postoperative specimen. The association of change in tumor stiffness between pre- and post-two cycles of NACT with final pathological response status was assessed.

RESULTS

The mean change in SWV before and after completion of two cycles of NACT was 0.42 ± 0.16 and 0.17 ± 0.11 m/s in responders and nonresponders, respectively, and this difference was proven to be statistically significant (p < 0.001) suggesting that tumors that exhibit a larger reduction in tumor stiffness, respond better. An SWV reduction cut-off of 0.295 m/s between baseline and post-two cycles of NACT was also arrived at, which can discriminate between responders and nonresponders with a sensitivity and specificity of 88% and 83%, respectively.

CONCLUSION

Difference in tumor stiffness measured by ARFI imaging before and after two cycles of chemotherapy can be used as a reliable early predictor of response to chemotherapy in breast cancer.

Accuracy of ultrasound elastography for predicting breast cancer response to neoadjuvant chemotherapy: A systematic review and meta-analysis

BACKGROUND

Several studies have reported the prognostic value of ultrasound elastography (UE) in patients receiving neoadjuvant chemotherapy (NACT) for breast cancer. However, the assessment of parameters differed between shear-wave elastography and strain elastography in terms of measured elasticity parameter and mode of imaging. It is important, therefore, to assess the accuracy of the two modes of elastography.

AIM

To assess the accuracy of UE for predicting the pathologic complete response (pCR) in breast cancer patients following NACT.

METHODS

A comprehensive and systematic search was performed in the databases of MEDLINE, EMBASE, SCOPUS, PubMed Central, CINAHL, Web of Science and Cochrane library from inception until December 2020. Meta-analysis was performed using STATA software “Midas” package.

RESULTS

A total of 14 studies with 989 patients were included. The pooled sensitivities were 86% [95% confidence interval (CI): 76%-92%] for UE, 77% (95%CI: 68%-84%) for shear-wave elastography, and 92% (95%CI: 73%-98%) for strain-wave elastography. The pooled score specificities were 86% (95%CI: 80%-90%) for UE, 84% (95%CI: 72%-91%) for shear-wave elasticity, and 87% (95%CI: 81%-92%) for strain-wave elastography. A significant heterogeneity was found among studies based on the chi-square test results and an I² statistic > 75%.

CONCLUSION

Strain-wave type of UE can accurately predict the pCR following NACT amongst breast cancer patients. Studies exploring its accuracy in different ethnic populations are required to strengthen the evidence.

The Role of Extracellular Matrix Proteins in Breast Cancer

The extracellular matrix is a structure composed of many molecules, including fibrillar (types I, II, III, V, XI, XXIV, XXVII) and non-fibrillar collagens (mainly basement membrane collagens: types IV, VIII, X), non-collagenous glycoproteins (elastin, laminin, fibronectin, thrombospondin, tenascin, osteopontin, osteonectin, entactin, periostin) embedded in a gel of negatively charged water-retaining glycosaminoglycans (GAGs) such as non-sulfated hyaluronic acid (HA) and sulfated GAGs which are linked to a core protein to form proteoglycans (PGs). This highly dynamic molecular network provides critical biochemical and biomechanical cues that mediate the cell–cell and cell–matrix interactions, influence cell growth, migration and differentiation and serve as a reservoir of cytokines and growth factors’ action. The breakdown of normal ECM and its replacement with tumor ECM modulate the tumor microenvironment (TME) composition and is an essential part of tumorigenesis and metastasis, acting as key driver for malignant progression. Abnormal ECM also deregulate behavior of stromal cells as well as facilitating tumor-associated angiogenesis and inflammation. Thus, the tumor matrix modulates each of the classically defined hallmarks of cancer promoting the growth, survival and invasion of the cancer. Moreover, various ECM-derived components modulate the immune response affecting T cells, tumor-associated macrophages (TAM), dendritic cells and cancer-associated fibroblasts (CAF). This review article considers the role that extracellular matrix play in breast cancer. Determining the detailed connections between the ECM and cellular processes has helped to identify novel disease markers and therapeutic targets.

Usefulness of new shear wave elastography in early predicting the efficacy of neoadjuvant chemotherapy for patients with breast cancer: where and when to measure is optimal?

BACKGROUND

The aim of this study was to investigate the diagnosis performance of new shear wave elastography (sound touch elastography, STE) in the prediction of neoadjuvant chemotherapy (NAC) response at an early stage in breast cancer patients and to determine the optimal measurement locations around the lesion in different ranges.

METHODS

One hundred and eight patients were analyzed in this prospective study from November 2018 to December 2020. All patients completed NAC treatment and underwent STE examination at three time points [the day before NAC (t0); the day before the second course (t1); the day before third course (t2)]. The stiffness of the whole lesion (G), 1-mm shell (S1) and 2-mm shell (S2) around the lesion was expressed by STE parameters. The relative changes (∆stiffness) of STE parameters after the first and second course of NAC were calculated and shown as the variables [Δ(t1) and Δ(t2)]. The diagnostic accuracy of STE was evaluated by means of receiver operating characteristic curve analysis.

RESULTS

The ∆stiffness (%) including ∆G_mean(t2), ∆S1_mean(t2) and ∆S2_mean(t2) all showed significant differences between pathological complete response (pCR) and non-pCR groups. ∆S2_mean(t2) displayed the best predictive performance for pCR (AUC = 0.842) with an ideal ∆stiffness threshold value - 26%.

CONCLUSIONS

Measuring the relative changes in the stiffness of surrounding tissue or entire lesion with STE holds promise for effectively predicting the response to NAC at its early stage for breast cancer patients and ∆stiffness of shell 2 mm after the second course of NAC may be a potential prediction parameter.

A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.

Parallels between the extracellular matrix roles in developmental biology and cancer biology

Interaction of a tumor with its microenvironment is an emerging field of investigation, and the crosstalk between cancer cells and the extracellular matrix is of particular interest, since cancer patients with abundant and stiff extracellular matrices display a poorer prognosis. At the post-juvenile stage, the extracellular matrix plays predominantly a structural role by providing support to cells and tissues; however, during development, matrix proteins exert a plethora of diverse signals to guide the movement and determine the fate of pluripotent cells. Taking a closer look at the communication between the extracellular matrix and cells of a developing body may bring new insights into cancer biology and identify cancer weaknesses. This review discusses parallels between the extracellular matrix roles during development and tumor growth.

Combining First- and Second-Order Continuity Constraints in Ultrasound Elastography

Ultrasound elastography is a prominent noninvasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, the edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique: Second-Order Ultrasound eLastography (SOUL). Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE, and MPWC-Net++. SOUL yields 27.72%, 62.56%, and 81.37% improvements of the signal-to-noise ratio (SNR) and 72.35%, 54.03%, and 65.17% improvements of the contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom, and in vivo tissue, respectively. The SOUL code can be downloaded from code.sonography.ai.

The matrix in cancer

The extracellular matrix is a fundamental, core component of all tissues and organs, and is essential for the existence of multicellular organisms. From the earliest stages of organism development until death, it regulates and fine-tunes every cellular process in the body. In cancer, the extracellular matrix is altered at the biochemical, biomechanical, architectural and topographical levels, and recent years have seen an exponential increase in the study and recognition of the importance of the matrix in solid tumours. Coupled with the advancement of new technologies to study various elements of the matrix and cell-matrix interactions, we are also beginning to see the deployment of matrix-centric, stromal targeting cancer therapies. This Review touches on many of the facets of matrix biology in solid cancers, including breast, pancreatic and lung cancer, with the aim of highlighting some of the emerging interactions of the matrix and influences that the matrix has on tumour onset, progression and metastatic dissemination, before summarizing the ongoing work in the field aimed at developing therapies to co-target the matrix in cancer and cancer metastasis.

Biomechanical sensing of in vivo magnetic nanoparticle hyperthermia-treated melanoma using magnetomotive optical coherence elastography

Rationale: Magnetic nanoparticle hyperthermia (MH) therapy is capable of thermally damaging tumor cells, yet a biomechanically-sensitive monitoring method for the applied thermal dosage has not been established. Biomechanical changes to tissue are known indicators for tumor diagnosis due to its association with the structural organization and composition of tissues at the cellular and molecular level. Here, by exploiting the theranostic functionality of magnetic nanoparticles (MNPs), we aim to explore the potential of using stiffness-based metrics that reveal the intrinsic biophysical changes of in vivo melanoma tumors after MH therapy. Methods: A total of 14 melanoma-bearing mice were intratumorally injected with dextran-coated MNPs, enabling MH treatment upon the application of an alternating magnetic field (AMF) at 64.7 kHz. The presence of the MNP heating sources was detected by magnetomotive optical coherence tomography (MM-OCT). For the first time, the elasticity alterations of the hyperthermia-treated, MNP-laden, in vivo tumors were also measured with magnetomotive optical coherence elastography (MM-OCE), based on the mechanical resonant frequency detected. To investigate the correlation between stiffness changes and the intrinsic biological changes, histopathology was performed on the excised tumor after the in vivo measurements. Results: Distinct shifts in mechanical resonant frequency were observed only in the MH-treated group, suggesting a heat-induced stiffness change in the melanoma tumor. Moreover, tumor cellularity, protein conformation, and temperature rise all play a role in tumor stiffness changes after MH treatment. With low cellularity, tumor softens after MH even with low temperature elevation. In contrast, with high cellularity, tumor softening occurs only with a low temperature rise, which is potentially due to protein unfolding, whereas tumor stiffening was seen with a higher temperature rise, likely due to protein denaturation. Conclusions: This study exploits the theranostic functionality of MNPs and investigates the MH-induced stiffness change on in vivo melanoma-bearing mice with MM-OCT and MM-OCE for the first time. It was discovered that the elasticity alteration of the melanoma tumor after MH treatment depends on both thermal dosage and the morphological features of the tumor. In summary, changes in tissue-level elasticity can potentially be a physically and physiologically meaningful metric and integrative therapeutic marker for MH treatment, while MM-OCE can be a suitable dosimetry technique.

Predicting Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer After the Second Cycle of Chemotherapy Using Shear-Wave Elastography-A Preliminary Evaluation

ABSTRACT

The primary objective of the study was to determine whether shear wave elastography can be used to predict the response of neoadjuvant chemotherapy (NACT) in women having invasive breast cancer. A prospective study involving 28 patients having invasive breast cancer and undergoing NACT followed by surgery was done after institutional review board approval. All the patients underwent 2-dimensional B-mode ultrasound and 2-dimensional shear wave elastography before the start of chemotherapy and after 2 cycles of completion of chemotherapy, and mean stiffness was recorded. The patients were segregated to responders and nonresponders based on residual cancer burden scoring. Difference in mean elasticity was compared between the 2 groups. The results showed that the mean stiffness after 2 cycles was significantly different between the responders and nonresponders and so was the change in the mean stiffness after 2 cycles of NACT. Using a cutoff value of 45.5 kPa (20.53%), change in mean elasticity after 2 cycles of NACT, sensitivity of 76.9%, and specificity of 80% with negative predictive value of 80.1 was attained. Responders show greater change in mean stiffness after 2 cycles of NACT as compared with nonresponders on shear wave elastography; thus, it can be used to predict response to NACT after 2 cycles.

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer

Objective

To investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer.

Patients and Methods

This prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1-t5), and at the end of NACT (t6) by shear-wave elastography and optical imaging. Ki-67 expression was quantitatively evaluated by immunohistochemistry using core biopsy specimens obtained before NACT. Pathological responses were evaluated by residual cancer burden. The ability of tumor stiffness, BS, Ki-67, and predRCB-which combined ΔEmean (t2) (the relative changes in Emean after the second NACT cycle), BS2 (BS after the second NACT cycle), and Ki-67-in predicting tumor responses was compared using receiver operating characteristic curves and the Z-test.

Results

Tumor stiffness and BS decreased during NACT. ΔEmean (t2), BS2, and Ki-67 had better predictive performance than other indexes in identifying a favorable response (AUC = 0.82, 0.81, and 0.80) and resistance responses (AUC = 0.85, 0.79, and 0.84), with no significant differences between the three (p > 0.05). PredRCB had better predictive performance than any parameter alone for a favorable response (AUC = 0.90) and resistance (AUC = 0.93).

Conclusion

Tumor stiffness, BS, and Ki-67 expression showed good and similar abilities for predicting the pathological response to NACT, and predRCB was a significantly better predictor than each index alone. These results may help design therapeutic strategies for breast cancer patients undergoing NACT.

A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy

OBJECTIVE

To evaluate the combination of tumor volume and sound speed as a potential imaging marker for assessing neoadjuvant chemotherapy (NAC) response.

METHODS

This study was carried out under an IRB-approved protocol (written consent required). Fourteen patients undergoing NAC for invasive breast cancer were examined with ultrasound tomography (UST) throughout their treatment. The volume (V) and the volume-averaged sound speed (VASS) of the tumors and their changes were measured for each patient. Time-dependent response curves of V and VASS were constructed individually for each patient and then as averages for the complete versus partial response groups in order to characterize differences between the two groups. Differences in group means were assessed for statistical significance using t-tests. Differences in shapes of group curves were evaluated with Kolmogorov-Smirnoff tests.

RESULTS

On average, tumor volume and sound speed in the partial response group showed a gradual decline in the first 60 days of treatment, while the complete response group showed a much steeper decline (P < 0.05). The shapes of the response curves of the two groups, corresponding to the entire treatment period, were also found to be significantly different (P < 0.05). Furthermore, large simultaneous drops in volume and sound speed in the first 3 weeks of treatment were characteristic only of the complete responders (P < 0.05).

CONCLUSION

This study demonstrates the feasibility of using UST to monitor NAC response, warranting future studies to better define the potential of UST for noninvasive, rapid identification of partial versus complete responders in women undergoing NAC.

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.

MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions

Recognizing that breast cancers present as firm, stiff lesions, the foundation of breast magnetic resonance elastography (MRE) is to combine tissue stiffness parameters with sensitive breast MR contrast-enhanced imaging. Breast MRE is a non-ionizing, cross-sectional MR imaging technique that provides for quantitative viscoelastic properties, including tissue stiffness, elasticity, and viscosity, of breast tissues. Currently, the technique continues to evolve as research surrounding the use of MRE in breast tissue is still developing. In the setting of a newly diagnosed cancer, associated desmoplasia, stiffening of the surrounding stroma, and necrosis are known to be prognostic factors that can add diagnostic information to patient treatment algorithms. In fact, mechanical properties of the tissue might also influence breast cancer risk. For these reasons, exploration of breast MRE has great clinical value. In this review, we will: (1) address the evolution of the various MRE techniques; (2) provide a brief overview of the current clinical studies in breast MRE with interspersed case examples; and (3) suggest directions for future research.

Prediction of pathological complete response in breast cancer patients during neoadjuvant chemotherapy: Is shear wave elastography a useful tool in clinical routine?

OBJECTIVE

To compare the validity of Shear Wave Elastography (SWE) for the preoperative assessment of pathological complete response (pCR) to standard clinical assessment in breast cancer patients undergoing neoadjuvant chemotherapy (NACT).

MATERIALS AND METHODS

This prospective, consecutive clinical trial was conducted under routine clinical practice. Analysis included 134 patients. SWE served as index test, final pathology from surgical specimen as reference standard. PCR (ypT0) was defined as primary endpoint. Elasticity changes were compared for the pCR- vs. non-pCR group. To determine the validity of shear wave velocity (V_s), ROC analyses and diagnostic accuracy parameters were calculated and compared to the final standard clinical assessment by physical examination, mammography and B-mode ultrasound (ycT + vs. ycT0).

RESULTS

V_s was significantly reduced in pCR and non-pCR groups during NACT (pCR: ΔV_s(abs) = 3.90 m/s, p < 0.001; non-pCR: ΔV_s(abs) = 3.10 m/s, p < 0.001). The pCR-group showed significant lower V_s for all control visits (t_1,2,END: p < 0.001). ROC analysis of V_s yielded moderate AUCs for the total population (t₀: 0.613, t₁: 0.745, t₂: 0.685, t_END: 0.718). Compared to standard clinical assessment, V_s(t_END) (cut-off: ≤3.35 m/s) was superior in sensitivity (79.6 % vs. 54.5 %), NPV (86.4 % vs. 77.5 %), FNR (20.4 % vs. 45.5 %), inferior in specificity (58.6 % vs. 77.5 %), PPV (46.3 % vs. 54.5 %), FPR (41.4 % vs. 22.5 %).

CONCLUSION

SWE measures significant differences in tumour elasticity changes in pCR vs. non-pCR cases. SWE shows improved sensitivity compared to standard clinical assessment, high NPV and low FNR, but failed in specificity in order to predict pCR under routine conditions.

Evaluation of therapeutic effect of targeting nanobubbles conjugated with NET-1 siRNA by shear wave elastography: an in vivo study of hepatocellular carcinoma bearing mice model

This study aimed at investigating the tumor stiffness of hepatocellular carcinoma (HCC) bearing mice model in vivo to evaluate the therapeutic efficacy of targeting nanobubbles (TNBS) conjugated with NET-1 siRNA (NET-1 siRNA-TNBS). Also tested whether shear wave elastography (SWE) could demonstrate the pathological tumor changes and used to monitor therapeutic efficacy as a noninvasive method. The HCC bearing mice model was established by injecting human HCC cell line (HepG2). The mice were then divided into three groups randomly, and were treated with TNBS conjugated with NET-1 siRNA, TNBS conjugated with negative control gene, and saline as control. US-SWE was performed for three times. SWE values of all the tumors in three groups were increased with tumor growth. Emax was correlated with tumor size (p < .05). NET-1 gene (treatment group) significantly delayed the growth of tumor size compared to other two groups (p < .0001), showing a significantly increased Emax (p < .05). Immunohistochemical results showed that the NET-1 protein expression was significantly lower than the negative control and blank groups. In conclusion, TNBS conjugated with NET-1 siRNA inhibited tumor growth and prolonged the life of experimental animals. SWE provided a noninvasive and real time imaging method to detect the changes in tumor development.

Framing cancer progression: influence of the organ- and tumour-specific matrisome

The extracellular matrix (ECM) plays a crucial role in regulating organ homeostasis. It provides mechanical and biochemical cues directing cellular behaviour and, therefore, has control over the progression of diseases such as cancer. Recent efforts have greatly enhanced our knowledge of the protein composition of the ECM and its regulators, the so-called matrisome, in healthy and cancerous tissues; yet, an overview of the common signatures and organ-specific ECM in cancer is missing. Here, we address this by taking a detailed approach to review why cancer grows in certain organs, and focus on the influence of the matrisome at primary and metastatic tumour sites. Our in-depth and comprehensive review of the current literature and general understanding identifies important commonalities and distinctions, providing insight into the biology of metastasis, which could pave the way to improve future diagnostics and therapies.

Harmonic Motion Imaging of Pancreatic Tumor Stiffness Indicates Disease State and Treatment Response

PURPOSE

Pancreatic ductal adenocarcinoma (PDA) is a common, deadly cancer that is challenging both to diagnose and to manage. Its hallmark is an expansive, desmoplastic stroma characterized by high mechanical stiffness. In this study, we sought to leverage this feature of PDA for two purposes: differential diagnosis and monitoring of response to treatment.

EXPERIMENTAL DESIGN

Harmonic motion imaging (HMI) is a functional ultrasound technique that yields a quantitative relative measurement of stiffness suitable for comparisons between individuals and over time. We used HMI to quantify pancreatic stiffness in mouse models of pancreatitis and PDA as well as in a series of freshly resected human pancreatic cancer specimens.

RESULTS

In mice, we learned that stiffness increased during progression from preneoplasia to adenocarcinoma and also effectively distinguished PDA from several forms of pancreatitis. In human specimens, the distinction of tumors versus adjacent pancreatitis or normal pancreas tissue was even more stark. Moreover, in both mice and humans, stiffness increased in proportion to tumor size, indicating that tuning of mechanical stiffness is an ongoing process during tumor progression. Finally, using a brca2-mutant mouse model of PDA that is sensitive to cisplatin, we found that tissue stiffness decreases when tumors respond successfully to chemotherapy. Consistent with this observation, we found that tumor tissues from patients who had undergone neoadjuvant therapy were less stiff than those of untreated patients.

CONCLUSIONS

These findings support further development of HMI for clinical applications in disease staging and treatment response assessment in PDA.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography

Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.

Strain wave elastography in response assessment to neo-adjuvant chemotherapy in patients with locally advanced breast cancer

OBJECTIVE

The study was conducted to study the role of strain wave elastography in evaluating the response to neo-adjuvant chemotherapy (NACT) in patients with locally advanced breast cancer (LABC).

METHODS

In this Institutional review board approved study, 86 patients of LABC were investigated with strain wave elastography. Females receiving NACT had the affected breast scanned by strain wave elastography before each cycle of chemotherapy and immediately before surgery by two independent observers. Changes in elastographic parameters (size ratio, strain ratio) were documented and then compared to clinical and pathologic tumor response as evaluated after mastectomy.

RESULTS

Elastographic strain ratio parameters demonstrated high sensitivity and moderate specificity for determining response even after the first cycle of neo-adjuvant chemotherapy [97.7% sensitivity (Sn), 68.7% specificity (Sp)]. Elastographic size ratio parameters showed moderate sensitivity and specificity for response detection after second and third cycle of neo-adjuvant chemotherapy (Sn, Sp: after second cycle of NACT Sn 83.3% Sp 80%; after third cycle of NACT Sn 77.8% Sp 100%).

CONCLUSION

Strain ratio is the earliest predictor of treatment response in patients of LABC. Serial imaging with elastography has the potential to predict treatment response early during the course of NACT, which may prove vital in management of patients with breast cancer.

ADVANCES IN KNOWLEDGE

Strain wave elastography is a powerful tool to predict chemoresponse early during the course of management, thereby providing an optimal window to change treatment protocols.

Evaluation of the response of breast cancer patients to neoadjuvant chemotherapy by combined contrast-enhanced ultrasonography and ultrasound elastography

The purpose of the present study was to investigate whether contrast-enhanced ultrasonography (CEUS) in combination with ultrasound elastography (UE) is able to accurately predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 65 breast cancer patients who received NAC at the First Affiliated Hospital of Zhejiang University (Hangzhou, China) between February 2016 and August 2017 and were recruited for the present study. Prior to and after NAC, examination by CEUS, UE or their combination was performed. Pathological results were obtained at the end of each chemotherapy cycle, based on which 41 cases were assigned to the response group and 24 to the non-response group. Kappa values were 0.710, 0.434 and 0.836 for CEUS, UE and CEUS+UE, respectively. The area under the receiver operating characteristic curves for CEUS, UE and CEUS+UE for determining the response to NAC was 0.864 [95% confidence interval (CI), 0.765–0.964], 0.715 (95% CI, 0.579–0.850) and 0.910 (95% CI, 0.826–0.993), respectively. It was identified that the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of CEUS+UE were higher than those of CEUS and US individually. The prediction accuracy was 89.2, 90.8 and 100% for CEUS, UE and their combination, respectively. CEUS and UE have their own advantages in evaluating the clinical efficacy of NAC in breast cancer, and a higher accuracy was achieved when the two techniques were applied in combination. Therefore, a combination of CEUS and UE may be a preferred method for the clinical assessment of the efficacy of NAC in breast cancer patients.

Ultrasound elastography as an imaging biomarker for detection of early tumor response to chemotherapy in a murine breast cancer model: a feasibility study

OBJECTIVE

This study investigated the feasibility of using strain elastography (SE) and real time shear wave elastography (RT-SWE) to evaluate early tumor response to cytotoxic chemotherapy in a murine xenograft breast cancer tumor model.

METHODS

MCF-7 breast cancer-bearing nude mice were treated with either cisplatin 2 mg kg^-1 plus paclitaxel 10 mg kg^-1 (treatment group) or sterile saline (control group) once daily for 5 days. The tumor elasticity was measured by SE or RT-SWE before and after therapy. Tumor cell density was assessed by hematoxylin and eosin staining, and the ratio of collagen fibers in the tumor was evaluated by Van Gieson staining. The correlation between tumor elasticity, as determined by SE and SWE, as well as the pathological tumor responses were analyzed.

RESULTS

Chemotherapy significantly attenuated tumor growth compared to the control treatment (p < 0.05). Chemotherapy also significantly increased tumor stiffness (p < 0.05) and significantly decreased (p < 0.05) tumor cell density compared with the control. Moreover, chemotherapy significantly increased the ratio of collagen fibers (p < 0.05). Tumor stiffness was positively correlated with the ratio of collagen fibers but negatively correlated with tumor cell density.

CONCLUSION

The study suggests that ultrasound elastography by SE and SWE is a feasible tool for assessing early responses of breast cancer to chemotherapy in our murine xenograft model. Advances in knowledge: This study showed that the tumor elasticity determined by ultrasound elastography could be a feasible imaging biomarker for assessing very early therapeutic responses to chemotherapy.

Apparent Diffusion Coefficient Value to Evaluate Tumor Response After Neoadjuvant Chemotherapy in Patients with Breast Cancer

RATIONALE AND OBJECTIVES

This study explored tumor behavior in patients with breast cancer during neoadjuvant chemotherapy (NAC) by sequential measurements of tumor apparent diffusion coefficient (ADC) after each chemotherapy cycle. The aim was to determine if the tumor ADC is useful to differentiate complete pathological response (cPR) from partial pathological response (pPR) during NAC.

MATERIALS AND METHODS

A total of 16 cases (in 14 patients) with diagnosis of breast cancer eligible to receive NAC were included. There were 70 magnetic resonance imaging examinations performed, 5 for each patient, during NAC cycles. Diffusion-weighted imaging was performed on a 1.5T system (b values of 0 and 700s/mm²). Four ADC ratios between the five MRI examinations were obtained to assess ADC changes during NAC. Absence of invasive breast cancer at surgical specimens (Miller-Payne 5) was considered as cPR and was used as reference for ADC cutoff ratios.

RESULTS

In this study, we were able to differentiate between cPR and pPR, after two cycles of NAC until the end of NAC before surgery (ADC ratios 2-4). The thresholds to differentiate between cPR and pPR of ADC ratios 2, 3, and 4, were 1.14 × 10^-3mm²/s, 1.08 × 10^-3mm²/s, and 1.25 × 10^-3mm²/s, respectively, and have a cross-validated sensitivity and specificity of 79.2%, 79.7% (ADC ratio 2); 100%, 66.7% (ADC ratio 3); and 100%, 83.8% (ADC ratio 4), respectively.

CONCLUSIONS

The ADC ratios were useful to differentiate cPR from pPR in breast cancer tumors after NAC. Thus, it may be useful in tailoring treatment in these patients.

Deep reinforcement learning for automated radiation adaptation in lung cancer

PURPOSE

To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).

METHODS

In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL.

RESULTS

Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose escalation/de-escalation between 1.5 and 3.8 Gy, a range similar to that used in the clinical protocol. The same DQN yielded two patterns of dose escalation for the 34 test patients, but with different reward variants. First, using the baseline P+ reward function, individual adaptive fraction doses of the DQN had similar tendencies to the clinical data with an RMSE = 0.76 Gy; but adaptations suggested by the DQN were generally lower in magnitude (less aggressive). Second, by adjusting the P+ reward function with higher emphasis on mitigating local failure, better matching of doses between the DQN and the clinical protocol was achieved with an RMSE = 0.5 Gy. Moreover, the decisions selected by the DQN seemed to have better concordance with patients eventual outcomes. In comparison, the traditional temporal difference (TD) algorithm for reinforcement learning yielded an RMSE = 3.3 Gy due to numerical instabilities and lack of sufficient learning.

CONCLUSION

We demonstrated that automated dose adaptation by DRL is a feasible and a promising approach for achieving similar results to those chosen by clinicians. The process may require customization of the reward function if individual cases were to be considered. However, development of this framework into a fully credible autonomous system for clinical decision support would require further validation on larger multi-institutional datasets.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis.

Ultrasound Elastography of the Prostate Using an Unconstrained Modulus Reconstruction Technique: A Pilot Clinical Study

A novel full-inversion-based technique for quantitative ultrasound elastography was investigated in a pilot clinical study on five patients for non-invasive detection and localization of prostate cancer and quantification of its extent. Conventional-frequency ultrasound images and radiofrequency (RF) data (~5 MHz) were collected during mechanical stimulation of the prostate using a transrectal ultrasound probe. Pre and post-compression RF data were used to construct the strain images. The Young's modulus (YM) images were subsequently reconstructed using the derived strain images and the stress distribution estimated iteratively using finite element (FE) analysis. Tumor regions determined based on the reconstructed YM images were compared to whole-mount histopathology images of radical prostatectomy specimens. Results indicated that tumors were significantly stiffer than the surrounding tissue, demonstrating a relative YM of 2.5 ± 0.8 compared to normal prostate tissue. The YM images had a good agreement with the histopathology images in terms of tumor location within the prostate. On average, 76% ± 28% of tumor regions detected based on the proposed method were inside respective tumor areas identified in the histopathology images. Results of a linear regression analysis demonstrated a good correlation between the disease extents estimated using the reconstructed YM images and those determined from whole-mount histopathology images (r² = 0.71). This pilot study demonstrates that the proposed method has a good potential for detection, localization and quantification of prostate cancer. The method can potentially be used for prostate needle biopsy guidance with the aim of decreasing the number of needle biopsies. The proposed technique utilizes conventional ultrasound imaging system only while no additional hardware attachment is required for mechanical stimulation or data acquisition. Therefore, the technique may be regarded as a non-invasive, low cost and potentially widely-available clinical tool for prostate cancer diagnosis.

Strain elastography imaging for early detection and prediction of tumor response to concurrent chemo-radiotherapy in locally advanced cervical cancer: feasibility study

Background

To investigate the feasibility of strain elastography imaging in early detecting and predicting treatment response in patients receiving concurrent chemo-radiotherapy (CCRT) for locally advanced cervical cancer.

Methods

Between January 2015 and June 2016, 47 patients with locally advanced cervical cancer were enrolled in a feasibility study approved by the institutional review board. All patients underwent CCRT and received strain elastography examinations at 4 time points: pre-therapy (baseline), 1 week and 2 weeks during, as well as immediately post CCRT. Treatment response was evaluated by MRI at the time of diagnosis and immediately after CCRT. Based on the MRI findings, the treatment outcome was characterized as complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). Strain ratio of the normal parametrial tissue vs. cervical tumor was calculated and compared with the clinical outcome.

Results

Out of the 47 patients, 36 patients who completed all 4 examinations were included in the analyses: 25 were classified as CR, 11 as PR, and 0 in the SD/PD groups. Strain ratios were significantly different among the time points in both the CR group (F = 87.004, p < 0.001) and PR group (F = 38.317, p < 0.001). Strain ratios were significantly difference between the CR and PR groups (F = 7.203 p = 0.011). Strain ratios between the CR group and PR group were significantly different at 1 week after treatment initiation (p < 0.05). Compared to the baseline, a significant decrease in the CR group was observed at week 1, week 2 and post treatment (all p < 0.001), while a significant decrease in the PR group was shown in week 2 and post treatment (both p < 0.05), but not at week 1 during CCRT (p = 0.084).

Conclusions

We have conducted a prospective longitudinal study to evaluate tumor response in women receiving CCRT for cervical cancers. This study has demonstrated the potential of strain elastography imaging in monitoring and early predicting tumor response induced by CCRT.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3411-5) contains supplementary material, which is available to authorized users.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Correlation of Ultrasound Shear Wave Elastography with Pathological Analysis in a Xenografic Tumour Model

The objective of this study was to evaluate the potential value of ultrasound (US) shear wave elastography (SWE) in assessing the relative change in elastic modulus in colorectal adenocarcinoma xenograft models in vivo and investigate any correlation with histological analysis. We sought to test whether non-invasive evaluation of tissue stiffness is indicative of pathological tumour changes and can be used to monitor therapeutic efficacy. US-SWE was performed in tumour xenografts in 15 NCr nude immunodeficient mice, which were treated with either the cytotoxic drug, Irinotecan, or saline as control. Ten tumours were imaged 48 hours post-treatment and five tumours were imaged for up to five times after treatment. All tumours were harvested for histological analysis and comparison with elasticity measurements. Elastic (Young's) modulus prior to treatment was correlated with tumour volume (r = 0.37, p = 0.008). Irinotecan administration caused significant delay in the tumour growth (p = 0.02) when compared to control, but no significant difference in elastic modulus was detected. Histological analysis revealed a significant correlation between tumour necrosis and elastic modulus (r = -0.73, p = 0.026). SWE measurement provided complimentary information to other imaging modalities and could indicate potential changes in the mechanical properties of tumours, which in turn could be related to the stages of tumour development.

Normalization of compression-induced hemodynamics in patients responding to neoadjuvant chemotherapy monitored by dynamic tomographic optical breast imaging (DTOBI)

We characterize novel breast cancer imaging biomarkers for monitoring neoadjuvant chemotherapy (NACT) and predicting outcome. Specifically, we recruited 30 patients for a pilot study in which NACT patients were imaged using dynamic tomographic optical breast imaging (DTOBI) to quantify the hemodynamic changes due to partial mammographic compression. DTOBI scans were obtained pre-treatment (referred to as day 0), as well as 7 and 30 days into therapy on female patients undergoing NACT. We present data for the 13 patients who participated in both day 0 and 7 measurements and had evaluable data, of which 7 also returned for day 30 measurements. We acquired optical images over 2 minutes following 4-8 lbs (18-36 N) of compression. The timecourses of tissue-volume averaged total hemoglobin (HbT), as well as hemoglobin oxygen saturation (SO₂) in the tumor vs. surrounding tissues were compared. Outcome prediction metrics based on the differential behavior in tumor vs. normal areas for responders (>50% reduction in maximum diameter) vs. non-responders were analyzed for statistical significance. At baseline, all patients exhibit an initial decrease followed by delayed recovery in HbT, and SO₂ in the tumor area, in contrast to almost immediate recovery in surrounding tissue. At day 7 and 30, this contrast is maintained in non-responders; however, in responders, the contrast in hemodynamic time-courses between tumor and normal tissue starts decreasing at day 7 and substantially disappears at day 30. At day 30 into NACT, responding tumors demonstrate "normalization" of compression induced hemodynamics vs. surrounding normal tissue whereas non-responding tumors did not. This data suggests that DTOBI imaging biomarkers, which are governed by the interplay between tissue biomechanics and oxygen metabolism, may be suitable for guiding NACT by offering early predictions of treatment outcome.

Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images

PURPOSE

Changes in textural characteristics of diffuse optical spectroscopic (DOS) functional images, accompanied by alterations in their mean values, are demonstrated here for the first time as early surrogates of ultimate treatment response in locally advanced breast cancer (LABC) patients receiving neoadjuvant chemotherapy (NAC). NAC, as a standard component of treatment for LABC patient, induces measurable heterogeneous changes in tumor metabolism which were evaluated using DOS-based metabolic maps. This study characterizes such inhomogeneous nature of response development, by determining alterations in textural properties of DOS images apparent at early stages of therapy, followed later by gross changes in mean values of these functional metabolic maps.

METHODS

Twelve LABC patients undergoing NAC were scanned before and at four times after treatment initiation, and tomographic DOS images were reconstructed at each time. Ultimate responses of patients were determined clinically and pathologically, based on a reduction in tumor size and assessment of residual tumor cellularity. The mean-value parameters and textural features were extracted from volumetric DOS images for several functional and metabolic parameters prior to the treatment initiation. Changes in these DOS-based biomarkers were also monitored over the course of treatment. The measured biomarkers were applied to differentiate patient responses noninvasively and compared to clinical and pathologic responses.

RESULTS

Responding and nonresponding patients demonstrated different changes in DOS-based textural and mean-value parameters during chemotherapy. Whereas none of the biomarkers measured prior the start of therapy demonstrated a significant difference between the two patient populations, statistically significant differences were observed at week one after treatment initiation using the relative change in contrast/homogeneity of seven functional maps (0.001<p<0.049), and mean value of water content in tissue (p=0.010). The cross-validated sensitivity and specificity of these parameters at week one of therapy ranged between 80%-100% and 67%-100%, respectively. Higher levels of statistically significant differences were exhibited at week four after start of treatment, with cross-validated sensitivities and specificities ranging between 80% and 100% for three textural and three mean-value parameters. The combination of the textural and mean-value parameters in a "hybrid" profile could better separate the two patient populations early on during a course of treatment, with cross-validated sensitivities and specificities of up to 100% (p=0.001).

CONCLUSIONS

The results of this study suggest that alterations in textural characteristics of DOS images, in conjunction with changes in their mean values, can classify noninvasively the ultimate clinical and pathologic response of LABC patients to chemotherapy, as early as one week after start of their treatment. This provides a basis for using DOS imaging as a tool for therapy personalization.

Early Evaluation of Relative Changes in Tumor Stiffness by Shear Wave Elastography Predicts the Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer

OBJECTIVES

Neoadjuvant chemotherapy plays an important role in comprehensive therapy for breast cancer, but response prediction is imperfect. Shear wave elastography (SWE) is a novel technique that can quantitatively evaluate tissue stiffness. In this study, we sought to investigate the application value of SWE for early prediction of the response to neoadjuvant chemotherapy in patients with breast cancer.

METHODS

We prospectively evaluated tumor stiffness in 62 patients with breast cancer using SWE, which was performed at baseline and after the second cycle of neoadjuvant chemotherapy. After chemotherapy, all of the patients underwent surgery. We investigated the correlations between the relative changes in tumor stiffness (Δ stiffness) after 2 cycles of chemotherapy and the pathologic response to the therapy.

RESULTS

Compared with baseline values, tumor stiffness after 2 cycles of neoadjuvant chemotherapy was significantly decreased in responders (P < .001) but not in nonresponders (P = .172). The Δstiffness was significantly higher in responders (-42.194%) than in nonresponders (-23.593%; P = .001). As determined at either the baseline or after the second cycle of chemotherapy, tumor stiffness was significantly lower in responders than in nonresponders (P = .033 and .009, respectively). The Δ stiffness threshold for distinguishing between responders and nonresponders was -36.1% (72.92% sensitivity and 85.71% specificity). Furthermore, correlating Δ stiffness with clinical and pathologic characteristics, we found that estrogen and progesterone receptor expression showed statistically significant correlations with Δ stiffness (estrogen receptor, P = .008; progesterone receptor, P = .023).

CONCLUSIONS

Early evaluation of relative changes in tumor stiffness using SWE could effectively predict the response to neoadjuvant chemotherapy in patients with breast cancer and might indicate better therapeutic strategies on a timelier basis.

Comparison of qualitative and semiquantitative strain elastography in breast lesions for diagnostic accuracy

Background

Strain elastography can be purely qualitative or semiquantitative using both strain score and strain ratio. The aim of this study was to establish the accuracy of semiquantitative elastography using both strain score and strain ratio in differentiating benign from malignant breast masses. The diagnostic performance of the two methods was analysed for any statistically significant difference.

Methods

A prospective study was carried out from May to December 2014 in the University of Nairobi, Department of Diagnostic Imaging and Radiation Medicine. One hundred and eighteen patients referred for breast ultrasound following clinical detection of masses certified the inclusion criteria. All solid masses identified on grey scale imaging were subjected to strain elastography. Elastographic findings were represented in both strain score and strain ratio. Comparison of diagnostic performance with histological findings as the gold standard for all detected solid masses was done. Fisher’s exact test and receiver operating characteristics curves were applied for statistical analysis to look for any significant differences between the diagnostic performance of strain score and strain ratio.

Results

Out of the 118, three patients did not attend for all the examinations and three biopsy results were misplaced therefore analysis was done for 112 subjects. The sensitivity, specificity, positive predictive value and negative predictive value of elasticity strain (Ueno) score were 0.86, 0.96, 0.89 and 0.96 respectively. For the strain ratio the values were 0.93, 0.96, 0.90 and 0.96 respectively. Fisher’s exact test P values comparing the sensitivity and specificity were 0.69 and 1.00 respectively not considered significant at p 0.05 levels. The areas under the curve (AUCs) from the receiver operating characteristic (ROC) curves were 0.972 and 0.976 for strain score and ratio respectively with a strong Pearson’s correlation coefficient, r 0.79 indicating a high diagnostic accuracy for both methods but no statistically significant difference in performance.

Conclusion

Semiquantitative ultrasound elastography has good diagnostic accuracy in differentiating benign and malignant breast solid lesions and there is no statistically significant difference between strain score and strain ratio in sensitivity, specificity and accuracy.

Electronic supplementary material

The online version of this article (doi:10.1186/s40644-016-0070-8) contains supplementary material, which is available to authorized users.

Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy

PURPOSE

This study evaluated pathological response to neoadjuvant chemotherapy using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOSI) biomarkers in locally advanced breast cancer (LABC).

MATERIALS AND METHODS

The institution's ethics review board approved this study. Subjects (n = 22) gave written informed consent prior to participating. US and DOSI data were acquired, relative to the start of neoadjuvant chemotherapy, at weeks 0, 1, 4, 8 and preoperatively. QUS parameters including the mid-band fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumor ultrasound data using spectral analysis. In the same patients, DOSI was used to measure parameters relating to tumor hemoglobin and composition. Discriminant analysis and receiver-operating characteristic (ROC) analysis was used to classify clinical and pathological response during treatment and to estimate the area under the curve (AUC). Additionally, multivariate analysis was carried out for pairwise QUS/DOSI parameter combinations using a logistic regression model.

RESULTS

Individual QUS and DOSI parameters, including the (SI), oxy-hemoglobin (HbO2), and total hemoglobin (HbT) were significant markers for response after one week of treatment (p < 0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI + HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0.

CONCLUSIONS

QUS and DOSI demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Multivariate QUS and DOSI parameters increased the sensitivity and specificity of classifying LABC patients as early as one week after treatment.

Supersonic Shear Wave Elastography of Response to Anti-cancer Therapy in a Xenograft Tumor Model

Our objective was to determine if supersonic shear wave elastography (SSWE) can detect changes in stiffness of a breast cancer model under therapy. A human invasive carcinoma was implanted in 22 mice. Eleven were treated with an anti-angiogenic therapy and 11 with glucose for 24 d. Tumor volume and stiffness were assessed during 2 wk before treatment and 0, 7, 12, 20 and 24 d after the start of therapy using SSWE. Pathology was assessed after 12 and 24 d of treatment. We found that response to therapy was associated with early softening of treated tumors only, resulting in a significant difference from non-treated tumors after 12 d of treatment (p = 0.03). On pathology, large areas of necrosis were observed at 12 d in treated tumors. Although treatment was still effective, treated tumors subsequently stiffened during a second phase of the treatment (days 12-24), with a small amount of necrosis observed on pathology on day 24. In conclusion, SSWE was able to measure changes in the stiffness of tumors in response to anti-cancer treatment. However, stiffness changes associated with good response to treatment may change over time, and increased stiffness may also reflect therapy efficacy.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.

The extracellular matrix in breast cancer predicts prognosis through composition, splicing, and crosslinking

The extracellular matrix in the healthy breast has an important tumor suppressive role, whereas the abnormal ECM in tumors can promote aggressiveness, and has been linked to breast cancer relapse, survival and resistance to chemotherapy. This review article gives an overview of the elements of the ECM which have been linked to prognosis of breast cancers, including changes in ECM protein composition, splicing, and microstructure.

Tumor-extracellular matrix interactions: Identification of tools associated with breast cancer progression

Several evidences support the concept that cancer development and progression are not entirely cancer cell-autonomous processes, but may be influenced, and possibly driven, by cross-talk between cancer cells and the surrounding microenvironment in which, besides immune cells, stromal cells and extracellular matrix (ECM) play a major role in regulating distinct biologic processes. Stroma and ECM-related signatures proved to influence breast cancer progression, and to contribute to the identification of tumor phenotypes resistant to cytotoxic and hormonal treatments. The possible clinical implications of the interplay between tumor cells and the microenvironment, with special reference to ECM remodelling, will be discussed in this review.

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was [Formula: see text]. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.

Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture

Early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, in conjunction with changes in their mean values, are demonstrated here, for the first time, to be capable of predicting ultimate clinical/pathologic responses of breast cancer patients to chemotherapy. Mechanisms of cell death, induced by chemotherapy within tumor, introduce morphological alterations in cancerous cells, resulting in measurable changes in tissue echogenicity. We have demonstrated that the development of such changes is reflected in early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, followed by consequent changes in their mean values. The spectral/textural biomarkers derived on this basis have been demonstrated as non-invasive surrogates of breast cancer chemotherapy response. Particularly, spectral biomarkers sensitive to the size and concentration of acoustic scatterers could predict treatment response of patients with up to 80% of sensitivity and specificity (p=0.050), after one week within 3-4 months of chemotherapy. However, textural biomarkers characterizing heterogeneities in distribution of acoustic scatterers, could differentiate between treatment responding and non-responding patients with up to 100% sensitivity and 93% specificity (p=0.002). Such early prediction permits offering effective alternatives to standard treatment, or switching to a salvage therapy, for refractory patients.

Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters

Tumor response to neoadjuvant chemotherapy in patients (n=30) with locally advanced breast cancer (LABC) was examined using quantitative ultrasound. Three ultrasound backscatter parameters, the integrated backscatter coefficient (IBC), average scatterer diameter (ASD), and average acoustic concentration (AAC), were estimated from tumors prior to treatment and at four times during neoadjuvant chemotherapy treatment (weeks 0, 1, 4, 8, and prior to surgery) and compared to ultimate clinical and pathological tumor responses. Results demonstrated that among all parameters, AAC was the best indicator of tumor response early after starting treatment. The AAC parameter increased substantially in treatment-responding patients as early as one week after treatment initiation, further increased at week 4, and attained a maximum at week 8. In contrast, the backscatter parameters from non-responders did not show any changes after treatment initiation. The two patient populations exhibited a statistically significant difference in changes of AAC (p<0.001) and ASD (p=0.023) over all treatment times examined. The best prediction of treatment response was achieved with the combination of AAC and ASD at week 4 (82% sensitivity, 100% specificity, and 86% accuracy) of 12-18 weeks of treatment. The survival of patients with responsive ultrasound parameters was higher than patients with non-responsive ultrasound parameters (35 ± 11 versus 27 ± 11 months, respectively, p=0.043). This study demonstrates that ultrasound parameters derived from the ultrasound backscattered power spectrum can potentially serve as non-invasive early measures of clinical tumor response to chemotherapy treatments.

Elastography, a sensitive tool for the evaluation of neoadjuvant chemotherapy in patients with high-grade serous ovarian carcinoma

The aim of the present study was to evaluate tumor stiffness by ultrasound elastography, which has the potential to provide additional information that is useful in predicting the response to neoadjuvant chemotherapy (NACT) in high-grade serous ovarian carcinoma (HGSC) patients. In total, 32 patients with International Federation of Gynecology and Obstetrics stage III and IV epithelial ovarian cancer treated with NACT underwent transvaginal and transabdominal sonography, followed by elastography and finally, by interval cytoreductive surgery. Histopathological analysis revealed 24 (75%) HGSCs. The mean elasticity score was statistically higher for the post-NACT lesions than for the pre-NACT lesions (3.13±0.57 vs. 2.04±0.51, respectively; P<0.001). The median elasticity score for the pre-NACT lesions on the four-point scale was 2, and the score for the post-NACT lesions was 4. Cases of post-NACT with scores of 3 and 4 had a higher optimal cytoreduction rate than cases with scores of 1 and 2 (93.8 vs. 25.0%, respectively; P<0.001). When the post-NACT elasticity scores of 3 and 4 were used for the prediction of optimal cytoreduction, elastography exhibited 88.2% sensitivity, 85.7% specificity, a 93.8% positive predictive value, a 75.0% negative predictive value and 87.5% accuracy. The results of the current study suggested that elastography is a sensitive tool for the evaluation of NACT in patients with HGSC and that it may aid gynecologists in choosing the optimal cytoreduction.