Quantitative Micro-Elastography Enables In Vivo Detection of Residual Cancer in the Surgical Cavity during Breast-Conserving Surgery

Intraoperative Supine Breast MRI for Residual Tumor Assessment after Breast-Conserving Therapy in Early-Stage Breast Cancer

Purpose To evaluate the diagnostic performance of intraoperative supine MRI (isMRI) in identifying residual tumor burden immediately after breast-conserving therapy (BCT). Materials and Methods This single-institution prospective study (April 2012-December 2022) included 43 consecutive participants with stage 0-II breast cancer. Three participants with multicentric disease were excluded from the final analysis. Preoperative supine MRI was performed after standard preoperative prone MRI to compare tumor sizes and distances to the nipple, chest wall, and skin. After lumpectomy, the saline-filled surgical cavity was assessed for residual tumor at 3-T isMRI in the operating suite. Diagnostic accuracy of isMRI findings in identifying residual tumor at resection margins was assessed using histopathology of shave margin specimens as the reference standard. Performance metrics of isMRI and re-excision rates were analyzed at per-participant and per-margin levels. Results Forty participants (median age, 58.5 years; range, 40-76 years) with 44 breast cancers (36 unifocal and four multifocal) underwent BCT, all with a single lumpectomy site. Margin assessment using isMRI yielded accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 80%, 50%, 93%, 75%, and 81% per participant, respectively; and 93%, 52%, 97%, 65%, and 96% per margin. Second re-excision was avoided in two of six (33%) participants with true-positive isMRI readings, decreasing the final re-excision rate from 18% to 13%. Histopathology of six false-negative isMRI cases revealed residual invasive carcinomas, all smaller than 0.3 cm, or intermediate-to-high grade ductal carcinoma in situ. Conclusion Intraoperative assessment for residual tumor after BCT using isMRI demonstrated promising accuracy to guide targeted margin clearance within the same operation. Keywords: Breast, MR-Imaging, MR-Dynamic Contrast Enhanced, Oncology Supplemental material is available for this article. © RSNA, 2025.

A co-registration method to validate in vivo optical coherence tomography in the breast surgical cavity

Breast-conserving surgery accompanied by adjuvant radiotherapy is the standard of care for patients with early-stage breast cancer. However, re-excision is reported in 20-30 % of cases, largely because of close or involved tumor margins in the specimen. Several intraoperative tumor margin assessment techniques have been proposed to overcome this issue, however, none have been widely adopted. Furthermore, tumor margin assessment of the excised specimen provides only an indirect indication of residual cancer in the patient following excision of the primary tumor. Handheld optical coherence tomography (OCT) probes and their functional extensions have the potential to detect residual cancer in vivo in the surgical cavity. Until now, validation of in vivo OCT has been achieved through correlation with ex vivo histology performed on the specimen removed during surgery that is adjacent to the tissue scanned in vivo. However, this indirect approach cannot accurately validate in vivo imaging performance. To address this, we present a method for robust co-registration of in vivo OCT scans with histology performed, not on the main specimen, but on cavity shavings corresponding directly to the tissue scanned in vivo. In this approach, we use ex vivo OCT scans as an intermediary, surgical sutures as fiducial markers, and extend the in vivo field-of-view to 15 × 15 mm2 by acquiring partially overlapping scans. We achieved successful co-registration of 78 % of 139 in vivo OCT scans from 16 patients. We present a detailed analysis of three cases, including a case where a functional extension of OCT, quantitative micro-elastography, was performed.

Research progress of breast cancer surgery during 2010-2024: a bibliometric analysis

Purpose

This study seeks to systematically analyze the research literature pertaining to breast cancer surgery from 2010 to 2024, as indexed in the PubMed database, employing bibliometric methodologies.

Methods

Employing the "bibliometrix" package in the R programming language, alongside VOSviewer and CiteSpace software, this research conducted a comprehensive visual analysis of 1,195 publications. The analysis encompassed publication trends, collaborative networks, journal evaluation, author and institutional assessments, country-specific analyses, keyword exploration, and the identification of research hotspots.

Results

The study observed a rising trend in the number of publications related to breast cancer surgery. However, there was a concomitant decline in citation rates, potentially indicating either a saturation of the research field or a diminution in research quality. The United States, China, and Japan are the leading contributors to research output, with the United States showing the most extensive international collaboration. The University of California, University of Toronto, and University of Texas MD Anderson Cancer Center were the top institutions for the number of published papers. Through a comprehensive analysis of keywords, we have identified "breast cancer" "pain" "anxiety" "lymphedema" "mastectomy" and "surgery" as central research themes within this domain, the corresponding clusters were subjected to analysis.

Conclusion

This study provides a comprehensive review of breast cancer surgery research, emphasizing major research areas and proposing future research directions. This study provides a significant resource for researchers and clinicians in the field.

Multimodal three-dimensional characterization of murine skeletal muscle micro-scale elasticity, structure, and composition: Impact of dysferlinopathy, Duchenne muscular dystrophy, and age on three hind-limb muscles

Skeletal muscle tissue function is governed by the mechanical properties and organization of its components, including myofibers, extracellular matrix, and adipose tissue, which can be modified by the onset and progression of many disorders. This study used a novel combination of quantitative micro-elastography and clearing-enhanced three-dimensional (3D) microscopy to assess 3D micro-scale elasticity and micro-architecture of muscles from two muscular dystrophies: dysferlinopathy and Duchenne muscular dystrophy, using male BLA/J and mdx mice, respectively, and their wild-type (WT) controls. We examined three muscles with varying proportions of slow- and fast-twitch myofibers: the soleus (predominantly slow), extensor digitorum longus (EDL; fast), and quadriceps (mixed), from BLA/J and WTBLA/J mice aged 3, 10, and 24 months, and mdx and WTmdx mice aged 10 months. Both dysferlin deficiency and age reduced the elasticity and variability of elasticity of the soleus and quadriceps, but not EDL. Overall, the BLA/J soleus was 20% softer than WT and less mechanically heterogeneous (-14% in standard deviation of elasticity). The BLA/J quadriceps at 24 months was 72% softer than WT and less mechanically heterogeneous (-59% in standard deviation), with substantial adipose tissue accumulation. While mdx muscles did not differ quantitatively from WT, regional heterogeneity was evident in micro-scale elasticity and micro-architecture of quadriceps (e.g., 11.2 kPa in a region with marked pathology vs 3.8 kPa in a less affected area). These results demonstrate differing biomechanical changes in hind-limb muscles of two distinct muscular dystrophies, emphasizing the potential for this novel multimodal technique to identify important differences between various myopathies.

A novel stress sensor enables accurate estimation of micro-scale tissue mechanics in quantitative micro-elastography

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to ex vivo studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for in vivo applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to ex vivo human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.

Prediction of disease-free survival using strain elastography and diffuse optical tomography in patients with T1 breast cancer: a 10-year follow-up study

BACKGROUND

Early-stage breast cancer (BC) presents a certain risk of recurrence, leading to variable prognoses and complicating individualized management. Yet, preoperative noninvasive tools for accurate prediction of disease-free survival (DFS) are lacking. This study assessed the potential of strain elastography (SE) and diffuse optical tomography (DOT) for non-invasive preoperative prediction of recurrence in T1 BC and developed a prediction model for estimating the probability of DFS.

METHODS

A total of 565 eligible patients with T1 invasive BC were enrolled prospectively and followed to investigate the recurrence. The associations between imaging features and DFS were evaluated and a best-prediction model for DFS was developed and validated.

RESULTS

During the median follow-up period of 10.8 years, 77 patients (13.6%) developed recurrences. The fully adjusted Cox proportional hazards model showed a significant trend between an increasing strain ratio (SR) (P < 0.001 for trend) and the total hemoglobin concentration (TTHC) (P = 0.001 for trend) and DFS. In the subgroup analysis, an intensified association between SR and DFS was observed among women who were progesterone receptor (PR)-positive, lower Ki-67 expression, HER2 negative, and without adjuvant chemotherapy and without Herceptin treatment (all P < 0.05 for interaction). Significant interactions between TTHC status and the lymphovascular invasion, estrogen receptor (ER) status, PR status, HER2 status, and Herceptin treatment were found for DFS(P < 0.05).The imaging-clinical combined model (TTHC + SR + clinicopathological variables) proved to be the best prediction model (AUC = 0.829, 95% CI = 0.786-0.872) and was identified as a potential risk stratification tool to discriminate the risk probability of recurrence.

CONCLUSION

The combined imaging-clinical model we developed outperformed traditional clinical prognostic indicators, providing a non-invasive, reliable tool for preoperative DFS risk stratification and personalized therapeutic strategies in T1 BC. These findings underscore the importance of integrating advanced imaging techniques into clinical practice and offer support for future research to validate and expand on these predictive methodologies.

In situ stress estimation in quantitative micro-elastography

In quantitative micro-elastography (QME), a pre-characterized compliant layer with a known stress-strain curve is utilized to map stress at the sample surface. However, differences in the boundary conditions of the compliant layer when it is mechanically characterized and when it is used in QME experiments lead to inconsistent stress estimation and consequently, inaccurate elasticity measurements. Here, we propose a novel in situ stress estimation method using an optical coherence tomography (OCT)-based uniaxial compression testing system integrated with the QME experimental setup. By combining OCT-measured axial strain with axial stress determined using a load cell in the QME experiments, we can estimate in situ stress for the compliant layer, more accurately considering its boundary conditions. Our proposed method shows improved accuracy, with an error below 10%, compared to 85% using the existing QME technique with no lubrication. Furthermore, demonstrations on hydrogels and cells indicate the potential of this approach for improving the characterization of the micro-scale mechanical properties of cells and their interactions with the surrounding biomaterial, which has potential for application in cell mechanobiology.

Tissue Elasticity as a Diagnostic Marker of Molecular Mutations in Morphologically Heterogeneous Colorectal Cancer

The presence of molecular mutations in colorectal cancer (CRC) is a decisive factor in selecting the most effective first-line therapy. However, molecular analysis is routinely performed only in a limited number of patients with remote metastases. We propose to use tissue stiffness as a marker of the presence of molecular mutations in CRC samples. For this purpose, we applied compression optical coherence elastography (C-OCE) to calculate stiffness values in regions corresponding to specific CRC morphological patterns (n = 54). In parallel to estimating stiffness, molecular analysis from the same zones was performed to establish their relationships. As a result, a high correlation between the presence of KRAS/NRAS/BRAF driver mutations and high stiffness values was revealed regardless of CRC morphological pattern type. Further, we proposed threshold stiffness values for label-free targeted detection of molecular alterations in CRC tissues: for KRAS, NRAS, or BRAF driver mutation-above 803 kPa (sensitivity-91%; specificity-80%; diagnostic accuracy-85%), and only for KRAS driver mutation-above 850 kPa (sensitivity-90%; specificity-88%; diagnostic accuracy-89%). To conclude, C-OCE estimation of tissue stiffness can be used as a clinical diagnostic tool for preliminary screening of genetic burden in CRC tissues.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

The LMIT: Light-mediated minimally-invasive theranostics in oncology

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Analysis of friction in quantitative micro-elastography

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique capable of measuring the mechanical properties of tissue on the micro-scale. As QME requires contact between the imaging window and the sample, the presence of friction affects the accuracy of the estimated elasticity. In previous implementations, a lubricant was applied at the contact surfaces, which was assumed to result in negligible friction. However, recently, errors in the estimation of elasticity caused by friction have been reported. This effect has yet to be characterized and is, therefore, not well understood. In this work, we present a systematic analysis of friction in QME using silicone phantoms. We demonstrate that friction, and, therefore, the elasticity accuracy, is influenced by several experimental factors, including the viscosity of the lubricant, the mechanical contrast between the compliant layer and the sample, and the time after the application of a compressive strain. Elasticity errors over an order of magnitude were observed in the absence of appropriate lubrication when compared to uniaxial compression testing. Using an optimized lubrication protocol, we demonstrate accurate elasticity estimation (<10% error) for nonlinear elastic samples with Young's moduli ranging from 3 kPa to 130 kPa. Finally, using a structured phantom, we demonstrate that friction can significantly reduce mechanical contrast in QME. We believe that the framework established in this study will facilitate more robust elasticity estimations in QME, as well as being readily adapted to understand the effects of friction in other contact elastography techniques.

Compression OCT-elastography combined with speckle-contrast analysis as an approach to the morphological assessment of breast cancer tissue

Currently, optical biopsy technologies are being developed for rapid and label-free visualization of biological tissue with micrometer-level resolution. They can play an important role in breast-conserving surgery guidance, detection of residual cancer cells, and targeted histological analysis. For solving these problems, compression optical coherence elastography (C-OCE) demonstrated impressive results based on differences in the elasticity of different tissue constituents. However, sometimes straightforward C-OCE-based differentiation is insufficient because of the similar stiffness of certain tissue components. We present a new automated approach to the rapid morphological assessment of human breast cancer based on the combined usage of C-OCE and speckle-contrast (SC) analysis. Using the SC analysis of structural OCT images, the threshold value of the SC coefficient was established to enable the separation of areas of adipose cells from necrotic cancer cells, even if they are highly similar in elastic properties. Consequently, the boundaries of the tumor bed can be reliably identified. The joint analysis of structural and elastographic images enables automated morphological segmentation based on the characteristic ranges of stiffness (Young's modulus) and SC coefficient established for four morphological structures of breast-cancer samples from patients post neoadjuvant chemotherapy (residual cancer cells, cancer stroma, necrotic cancer cells, and mammary adipose cells). This enabled precise automated detection of residual cancer-cell zones within the tumor bed for grading cancer response to chemotherapy. The results of C-OCE/SC morphometry highly correlated with the histology-based results (r =0.96-0.98). The combined C-OCE/SC approach has the potential to be used intraoperatively for achieving clean resection margins in breast cancer surgery and for performing targeted histological analysis of samples, including the evaluation of the efficacy of cancer chemotherapy.