Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer.

BACKGROUND: The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs). METHODS: Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration. RESULTS: Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability. CONCLUSION: The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.

Shape-recovery of implanted shape-memory devices remotely triggered via image-guided ultrasound heating.

Shape-memory materials hold great potential to impart medical devices with functionalities useful during implantation, locomotion, drug delivery, and removal. However, their clinical translation is limited by a lack of non-invasive and precise methods to trigger and control the shape recovery, especially for devices implanted in deep tissues. In this study, the application of image-guided high-intensity focused ultrasound (HIFU) heating is tested. Magnetic resonance-guided HIFU triggered shape-recovery of a device made of polyurethane urea while monitoring its temperature by magnetic resonance thermometry. Deformation of the polyurethane urea in a live canine bladder (5 cm deep) is achieved with 8 seconds of ultrasound-guided HIFU with millimeter resolution energy focus. Tissue sections show no hyperthermic tissue injury. A conceptual application in ureteral stent shape-recovery reduces removal resistance. In conclusion, image-guided HIFU demonstrates deep energy penetration, safety and speed.

The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer.

RATIONALE AND OBJECTIVES: To evaluate whether ultrasound-based radiomics features can effectively predict HER2-low expression in patients with breast cancer (BC). MATERIAL AND METHODS: Between January 2021 and June 2023, patients who received US scans with pathologically confirmed BC in this multicenter study were included. In total, 383 patients from institution 1 were comprised of training set, 233 patients from institution 2 were comprised of validation set and 149 patients from institution 3 were comprised of external validation set. Radiomics features were derived from conventional ultrasound (US) images. The minimum redundancy and maximum relevancy and the least absolute shrinkage and selector operation algorithm were used to generate an US-based radiomics score (RS). Multivariable logistic regression analysis was used to select variables associated with HER2 expressions. The diagnostic performance of the RS was evaluated through the area under the receiver operating characteristic curve (AUC). RESULTS: In the training set, the RS yield an AUC of 0.81 (95%CI: 0.76-0.84) for differentiation HER2-zero from HER2-low and -positive cases, and performed well in validation set (AUC 0.84, 95%CI: 0.78-0.88) and external validation set (AUC 0.82, 95%CI: 0.73-0.90). In the subgroups analysis, the RS showed good performance in distinguishing HER2-zero from HER2 1 + , HER2 2 + and HER2-low tumors (AUC range, 0.79-0.87). CONCLUSION: The RS based on conventional US is proven effective for predicting HER2-low expression in BC.

Reconstruction-driven motion estimation for motion-compensated MR CINE imaging.

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments.

Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI.

In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac motion and incorporates this information in the reconstruction. However, two obstacles prevent the practical use of MCMR in clinical situations: First, inaccurate motion estimation often leads to inferior CMR reconstruction results. Second, the motion estimation frequently leads to a long processing time for the reconstruction. In this work, we propose a learning-based and unrolled MCMR framework that can perform precise and rapid CMR reconstruction. We achieve accurate reconstruction by developing a joint optimization between the motion estimation and reconstruction, in which a deep learning-based motion estimation framework is unrolled within an iterative optimization procedure. With progressive iterations, a mutually beneficial interaction can be established in which the reconstruction quality is improved with more accurate motion estimation. Further, we propose a groupwise motion estimation framework to speed up the MCMR process. A registration template based on the cardiac sequence average is introduced, while the motion estimation is conducted between the cardiac frames and the template. By applying this framework, cardiac sequence registration can be accomplished with linear time complexity. Experiments on 43 in-house acquired 2D CINE datasets indicate that the proposed unrolled MCMR framework can deliver artifacts-free motion estimation and high-quality CMR reconstruction even for imaging acceleration rates up to 20x. We compare our approach with state-of-the-art reconstruction methods and it outperforms them quantitatively and qualitatively in all adapted metrics across all acceleration rates.

A biodegradable, flexible photonic patch for in vivo phototherapy.

Diagnostic and therapeutic illumination on internal organs and tissues with high controllability and adaptability in terms of spectrum, area, depth, and intensity remains a major challenge. Here, we present a flexible, biodegradable photonic device called iCarP with a micrometer scale air gap between a refractive polyester patch and the embedded removable tapered optical fiber. ICarP combines the advantages of light diffraction by the tapered optical fiber, dual refractions in the air gap, and reflection inside the patch to obtain a bulb-like illumination, guiding light towards target tissue. We show that iCarP achieves large area, high intensity, wide spectrum, continuous or pulsatile, deeply penetrating illumination without puncturing the target tissues and demonstrate that it supports phototherapies with different photosensitizers. We find that the photonic device is compatible with thoracoscopy-based minimally invasive implantation onto beating hearts. These initial results show that iCarP could be a safe, precise and widely applicable device suitable for internal organs and tissue illumination and associated diagnosis and therapy.

A honeybee stinger-inspired self-interlocking microneedle patch and its application in myocardial infarction treatment.

Weak tissue adhesion remains a major challenge in clinical translation of microneedle patches. Mimicking the structural features of honeybee stingers, stiff polymeric microneedles with unidirectionally backward-facing barbs were fabricated and embedded into various elastomer films to produce self-interlocking microneedle patches. The spirality of the barbing pattern was adjusted to increase interlocking efficiency. In addition, the micro-bleeding caused by microneedle puncturing adhered the porous surface of the patch substrate to the target tissue via coagulation. In the demonstrative application of myocardial infarction treatment, the bioinspired microneedle patches firmly fixed on challenging beating hearts, significantly reduced cardiac wall stress and strain in the infarct, and maintained left ventricular function and morphology. In addition, the microneedle patch was minimally invasively implanted onto beating porcine heart in 10 minutes, free of sutures and adhesives. Therefore, the honeybee stinger-inspired microneedles could provide an adaptive and convenient means to implant patches for various medical applications. STATEMENT OF SIGNIFICANCE: Adhesion between tissue and microneedle patches with smooth microneedles is usually weak. We introduce a novel barbing method of fabricating unidirectionally backward facing barbs with controllable spirality on the microneedles on microneedle patches. The microneedle patches self-interlock on mechanically dynamic beating hearts, similar to honeybee stingers. The micro-bleeding and coagulation on the porous surface provide additional adhesion force. The microneedle patches attenuate left ventricular remodeling via mechanical support and are compatible with minimally invasive implantation.

Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma.

OBJECTIVE: This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. METHODS: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. CONCLUSION: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. ADVANCES IN KNOWLEDGE: Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging.

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.

Preoperative ultrasound-based radiomics score can improve the accuracy of the Memorial Sloan Kettering Cancer Center nomogram for predicting sentinel lymph node metastasis in breast cancer.

PURPOSE: To develop a combined nomogram by incorporating the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram and ultrasound (US)-based radiomics score (Radscore) for predicting sentinel lymph node (SLN) metastasis in invasive breast cancer. MATERIALS AND METHODS: This retrospective study was approved by the ethics committee of our institution, and written informed consent was waived. A total of 452 patients with invasive breast cancer who received SLN Biopsy in a single center were included between January 2016 and December 2019. The patients were divided into a training set (n = 318) and a validation set (n = 134). A total of 1216 features were extracted from the regions of interest (ROIs) of the tumors on conventional ultrasound. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to build the Radscore. Afterward, the diagnostic performance was assessed and validated. Comparison of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were performed to evaluate the incremental value of the combined model. RESULTS: Obtained from 18 features, the Radscore indicated a favorable discriminatory capability in the training set with an area under the curve (AUC) of 0.834, whereas a value of 0.770 was observed in the validation set. The AUC of the combined model was 0.901 (95 % confidence interval (95 % CI): 0.865-0.938) in the training set and 0.833 (95 % CI: 0.788-0.878) in the validation set. Both of them were superior to MSKCC or imaging Radscore alone (P < 0.05). DCA demonstrated that the combined model was superior to the others in terms of clinical practicability. CONCLUSION: Preoperative US-based Radscore can improve the accuracy of clinical MSKCC nomogram for SLN metastasis prediction in breast cancer.

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning.

Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation of non-rigid motion fields from undersampled images at different respiratory positions (or bins). However, state-of-the-art registration methods can be time-consuming. This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained patch-wise to circumvent the challenges of training a 3D network volume-wise which requires large amounts of GPU memory and 3D datasets. We perform 5-fold cross-validation with 45 CMRA datasets and demonstrate that RespME-net can predict 3D non-rigid motion fields with subpixel accuracy (0.44 ± 0.38 mm) within ~10 seconds, being ~20 times faster than a GPU-implemented state-of-the-art non-rigid registration method. Moreover, we perform non-rigid motion-compensated CMRA reconstruction for 9 additional patients. The proposed RespME-net has achieved similar motion-corrected CMRA image quality to the conventional registration method regarding coronary artery length and sharpness.

Identification of lncRNAs Involved in PCV2 Infection of PK-15 Cells.

Porcine circovirus type 2 (PCV2) can cause severe disease in infected pigs, resulting in massive economic loss for the swine industry. Transcriptomic and proteomic approaches have been widely employed to identify the underlying molecular mechanisms of the PCV2 infection. Numerous differentially expressed mRNAs, miRNAs, and proteins, together with their associated signaling pathways, have been identified during PCV2 infection, paving the way for analysis of their biological functions. Long noncoding RNAs (lncRNAs) are important regulators of multiple biological processes. However, little is known regarding their role in the PCV2 infection. Hence, in our study, RNA-seq was performed by infecting PK-15 cells with PCV2. Analysis of the differentially expressed genes (DEGs) suggested that the cytoskeleton, apoptosis, cell division, and protein phosphorylation were significantly disturbed. Then, using stringent parameters, six lncRNAs were identified. Additionally, potential targets of the lncRNAs were predicted using both cis- and trans-prediction methods. Interestingly, we found that the HOXB (Homeobox B) gene cluster was probably the target of the lncRNA LOC106505099. Enrichment analysis of the target genes showed that numerous developmental processes were altered during PCV2 infection. Therefore, our study revealed that lncRNAs might affect porcine embryonic development through the regulation of the HOXB genes.

Analysis and modeling of moisture sorption behavior for antimicrobial composite protein films.

The WPI-NaCas-GLY antimicrobial film takes full advantage of the controlled release of active or antimicrobial agents as well as demonstrates a great potential for functioning as an alternative biodegradable polymer in practical applications. The moisture sorption kinetics of the film as an important carrier of active agents was investigated at various relative humidities (RH). The results indicated that the moisture sorption characterization and procedure of this film can be described well by the empirical Peleg model with higher confidence and concordance. The model could predict the film's moisture content at any time (Mt), the time to reach any given level of R (tR), the equilibrium moisture at any RH condition (Me), and isotherm trend based upon experimental data and modeled constants k(1), k(2), a, b, c, and d without giving consideration to their physical meaning. The water vapor transmission rate of the WPI-NaCas-GLY antimicrobial film increased exponentially with increasing RH due to its hydrophilicity, which was primarily caused by the presence of glycerol in a higher content. The results also suggested that aw predominately affects the film's Me values compared with the temperature factor by fixed nonlinear multiple regression analyses.