Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. METHODS: Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. RESULTS: In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). CONCLUSION: Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

Predictive testing for neurodegenerative diseases in the age of next-generation sequencing.

The availability and cost of next-generation sequencing (NSG) now allow testing large numbers of genes simultaneously. However, the gold standard for predictive testing has been to test only for a known family mutation or confirmed family disease. The goal of this study was to investigate the psychological impact of predictive testing for autosomal dominant neurodegenerative diseases without a known family mutation using next-generation sequencing panels compared to single-gene testing of a known family mutation. Fourteen individuals from families with a known mutation and 10 individuals with unknown family mutations participated. Participants completed questionnaires on demographics, genetic knowledge, and psychological measures of anxiety, depression, perceived personal control, rumination, and intolerance to uncertainty at baseline and 1 and 6 months after receiving results. Decision regret was measured 1 and 6 months after receiving results. Participants completed a modified Huntington disease genetic testing protocol with genetic counseling and neurological and psychological evaluation. Genetic testing of either the known family mutation or an NGS panel of neurodegenerative disease genes was performed. Semi-structured interviews were performed at 6 months post-results about their experience. Two-sample t tests were performed on data collected at each time point to identify significant between-group differences in demographic variables, baseline psychological scores, and baseline genetic knowledge scores. Within-group change over time was assessed by a mixed-effects model. Results of this study indicate that NGS panels for predictive testing for neurodegenerative disease are safe and beneficial to participants when performed within a modified HD protocol. Though significant differences in psychological outcomes were found, these differences may have been driven by genetic results and baseline psychological differences between individuals within the groups. Participants did not regret their decision to test and were largely pleased with the testing protocol.

Lack of aprataxin impairs mitochondrial functions via downregulation of the APE1/NRF1/NRF2 pathway.

Ataxia oculomotor apraxia type 1 (AOA1) is an autosomal recessive disease caused by mutations in APTX, which encodes the DNA strand-break repair protein aprataxin (APTX). CoQ10 deficiency has been identified in fibroblasts and muscle of AOA1 patients carrying the common W279X mutation, and aprataxin has been localized to mitochondria in neuroblastoma cells, where it enhances preservation of mitochondrial function. In this study, we show that aprataxin deficiency impairs mitochondrial function, independent of its role in mitochondrial DNA repair. The bioenergetics defect in AOA1-mutant fibroblasts and APTX-depleted Hela cells is caused by decreased expression of SDHA and genes encoding CoQ biosynthetic enzymes, in association with reductions of APE1, NRF1 and NRF2. The biochemical and molecular abnormalities in APTX-depleted cells are recapitulated by knockdown of APE1 in Hela cells and are rescued by overexpression of NRF1/2. Importantly, pharmacological upregulation of NRF1 alone by 5-aminoimidazone-4-carboxamide ribonucleotide does not rescue the phenotype, which, in contrast, is reversed by the upregulation of NRF2 by rosiglitazone. Accordingly, we propose that the lack of aprataxin causes reduction of the pathway APE1/NRF1/NRF2 and their target genes. Our findings demonstrate a critical role of APTX in transcription regulation of mitochondrial function and the pathogenesis of AOA1 via a novel pathomechanistic pathway, which may be relevant to other neurodegenerative diseases.

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis.

BACKGROUND: Diffuse optical spectroscopy (DOS) has been demonstrated capable of monitoring response to neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC) patients. In this study, we evaluate texture features of pretreatment DOS functional maps for predicting LABC response to NAC. METHODS: Locally advanced breast cancer patients (n=37) underwent DOS breast imaging before starting NAC. Breast tissue parametric maps were constructed and texture analyses were performed based on grey-level co-occurrence matrices for feature extraction. Ground truth labels as responders (R) or non-responders (NR) were assigned to patients based on Miller-Payne pathological response criteria. The capability of DOS textural features computed on volumetric tumour data before the start of treatment (i.e., 'pretreatment') to predict patient responses to NAC was evaluated using a leave-one-out validation scheme at subject level. Data were analysed using a logistic regression, naive Bayes, and k-nearest neighbour classifiers. RESULTS: Data indicated that textural characteristics of pretreatment DOS parametric maps can differentiate between treatment response outcomes. The HbO2 homogeneity resulted in the highest accuracy among univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) were 86.5% and 89.0%, respectively, and accuracy was 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-contrast+HbO2-homogeneity, which resulted in a %Sn/%Sp=78.0/81.0% and an accuracy of 79.5%. CONCLUSIONS: This study demonstrated that the pretreatment DOS texture features can predict breast cancer response to NAC and potentially guide treatments.

Functional cellular analyses reveal energy metabolism defect and mitochondrial DNA depletion in a case of mitochondrial aconitase deficiency.

Defects in the tricarboxylic acid cycle (TCA) are associated with a spectrum of neurological phenotypes that are often difficult to diagnose and manage. Whole-exome sequencing (WES) led to a rapid expansion of diagnostic capabilities in such disorders and facilitated a better understanding of disease pathogenesis, although functional characterization remains a bottleneck to the interpretation of potential pathological variants. We report a 2-year-old boy of Afro-Caribbean ancestry, who presented with neuromuscular symptoms without significant abnormalities on routine diagnostic evaluation. WES revealed compound heterozygous missense variants of uncertain significance in mitochondrial aconitase (ACO2), which encodes the TCA enzyme ACO2. Pathogenic variants in ACO2 have been described in a handful of families as the cause of infantile cerebellar-retinal degeneration syndrome. Using biochemical and cellular assays in patient fibroblasts, we found that ACO2 expression was quantitatively normal, but ACO2 enzyme activity was <20% of that observed in control cells. We also observed a deficiency in cellular respiration and, for the first time, demonstrate evidence of mitochondrial DNA depletion and altered expression of some TCA components and electron transport chain subunits. The observed cellular defects were completely restored with ACO2 gene rescue. Our findings demonstrate the pathogenicity of two VUS in ACO2, provide novel mechanistic insights to TCA disturbances in ACO2 deficiency, and implicate mitochondrial DNA depletion in the pathogenesis of this recently described disorder.

Mitochondrial myopathy with dystrophic features due to a novel mutation in the MTTM gene.

INTRODUCTION: A 61-year-old woman with a 5-year history of progressive muscle weakness and atrophy had a muscle biopsy characterized by a combination of dystrophic features (necrotic fibers and endomysial fibrosis) and mitochondrial alterations [ragged-red, cytochrome c oxidase (COX)-negative fibers]. METHODS: Sequencing of the whole mtDNA, assessment of the mutation load in muscle and accessible nonmuscle tissues, and single fiber polymerase chain reaction. RESULTS: Muscle mitochondrial DNA (mtDNA) sequencing revealed a novel heteroplasmic mutation (m.4403G>A) in the gene (MTTM) that encodes tRNA(Met). The mutation was not present in accessible nonmuscle tissues from the patient or 2 asymptomatic sisters. CONCLUSIONS: The clinical features and muscle morphology in this patient are very similar to those described in a previous patient with a different mutation, also in MTTM, which suggests that mutations in this gene confer a distinctive "dystrophic" quality. This may be a diagnostic clue in patients with isolated mitochondrial myopathy.

Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study.

Background: In patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy. Methods: The phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination. Results: Between June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation. Conclusion: The study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study. Clinical trial registration: clinicaltrials.gov, ID NCT04050228.

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

PURPOSE: Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. METHODS: The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. RESULTS: The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. CONCLUSION: The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

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.

Epidemiology and Histopathology of Nasopharyngeal Neoplasms in Iran.

Introduction: This study aimed to study the trend, histologic pattern, geographical distribution, and characteristics of nasopharyngeal carcinoma (NPC) and nasopharyngeal neoplasms (NPN) from 2003 to 2017 in Iran. Materials and Methods: The Ministry of Health and Medical Education collected NPN cases from the corresponding university in each province and stored them in Iran National Cancer Registry (INCR) database. The Joinpoint program calculated the average annual percent change (AAPC) and its 95% confidence interval (CI). The jump model minimized the interfering effect of INCR transformation. Results: 3653 NPN cases were reported between 2003-2010 and 2014-2017, with a mean age of 49.04 ± 18.31 years and a male-to-female ratio of 2.15. The age-standardized incidence rate (ASIR) per 100,000 person-years was 0.30 for females and 0.68 for males in 2017. Although the ASIR/100,000 of NPN raised from 0.35 to 0.49 during 2003-2017, the trend was constant with an AAPC of -2% (95% CI: -4.8% to 0.9%). The age-specific incidence rate was highest in the older than 70 population (1.56/100,000). NPC formed 77.1% of NPNs and showed a constant pattern (AAPC CI: -5.7% to 0.2%), in contrast to the significant increase of non-keratinizing squamous cell carcinoma (AAPC CI: 2.3%to 24.5%). Conclusions: Nasopharynx cancer is rare in Iran, and NPC incidence remained constant from 2003 to 2017, unlike previously reported rising trend. However, non-keratinizing squamous cell carcinoma exhibited a significant increase, and future studies are needed to examine the role of the Epstein-Barr virus on this growth rate.

A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI.

A noticeable proportion of larger brain metastases (BMs) are not locally controlled after stereotactic radiotherapy, and it may take months before local progression is apparent on standard follow-up imaging. This work proposes and investigates new explainable deep-learning models to predict the radiotherapy outcome for BM. A novel self-attention-guided 3D residual network is introduced for predicting the outcome of local failure (LF) after radiotherapy using the baseline treatment-planning MRI. The 3D self-attention modules facilitate capturing long-range intra/inter slice dependencies which are often overlooked by convolution layers. The proposed model was compared to a vanilla 3D residual network and 3D residual network with CBAM attention in terms of performance in outcome prediction. A training recipe was adapted for the outcome prediction models during pretraining and training the down-stream task based on the recently proposed big transfer principles. A novel 3D visualization module was coupled with the model to demonstrate the impact of various intra/peri-lesion regions on volumetric multi-channel MRI upon the network's prediction. The proposed self-attention-guided 3D residual network outperforms the vanilla residual network and the residual network with CBAM attention in accuracy, F1-score, and AUC. The visualization results show the importance of peri-lesional characteristics on treatment-planning MRI in predicting local outcome after radiotherapy. This study demonstrates the potential of self-attention-guided deep-learning features derived from volumetric MRI in radiotherapy outcome prediction for BM. The insights obtained via the developed visualization module for individual lesions can possibly be applied during radiotherapy planning to decrease the chance of LF.

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.

Automatic Assessment of Stereotactic Radiation Therapy Outcome in Brain Metastasis Using Longitudinal Segmentation on Serial MRI.

The standard clinical approach to assess the radiotherapy outcome in brain metastasis is through monitoring the changes in tumour size on longitudinal MRI. This assessment requires contouring the tumour on many volumetric images acquired before and at several follow-up scans after the treatment that is routinely done manually by oncologists with a substantial burden on the clinical workflow. In this work, we introduce a novel system for automatic assessment of stereotactic radiation therapy (SRT) outcome in brain metastasis using standard serial MRI. At the heart of the proposed system is a deep learning-based segmentation framework to delineate tumours longitudinally on serial MRI with high precision. Longitudinal changes in tumour size are then analyzed automatically to assess the local response and detect possible adverse radiation effects (ARE) after SRT. The system was trained and optimized using the data acquired from 96 patients (130 tumours) and evaluated on an independent test set of 20 patients (22 tumours; 95 MRI scans). The comparison between automatic therapy outcome evaluation and manual assessments by expert oncologists demonstrates a good agreement with an accuracy, sensitivity, and specificity of 91%, 89%, and 92%, respectively, in detecting local control/failure and 91%, 100%, and 89% in detecting ARE on the independent test set. This study is a step forward towards automatic monitoring and evaluation of radiotherapy outcome in brain tumours that can streamline the radio-oncology workflow substantially.

Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.

OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted therapies. This study aimed to evaluate the diagnostic performance of machine learning (ML) classification of newly diagnosed breast masses into TN versus non-TN (NTN) and HER2+ versus HER2 negative (HER2-) breast cancer, using radiomic features extracted from grayscale ultrasound (US) b-mode images. MATERIALS AND METHODS: A retrospective chart review identified 88 female patients who underwent diagnostic breast US imaging, had confirmation of invasive malignancy on pathology and receptor status determined on immunohistochemistry available. The patients were classified as TN, NTN, HER2+ or HER2- for ground-truth labelling. For image analysis, breast masses were manually segmented by a breast radiologist. Radiomic features were extracted per image and used for predictive modelling. Supervised ML classifiers included: logistic regression, k-nearest neighbour, and Naïve Bayes. Classification performance measures were calculated on an independent (unseen) test set. The area under the receiver operating characteristic curve (AUC), sensitivity (%), and specificity (%) were reported for each classifier. RESULTS: The logistic regression classifier demonstrated the highest AUC: 0.824 (sensitivity: 81.8%, specificity: 74.2%) for the TN sub-group and 0.778 (sensitivity: 71.4%, specificity: 71.6%) for the HER2 sub-group. CONCLUSION: ML classifiers demonstrate high diagnostic accuracy in classifying TN versus NTN and HER2+ versus HER2- breast cancers using US images. Identification of more aggressive breast cancer subtypes early in the diagnostic process could help achieve better prognoses by prioritizing clinical referral and prompting adequate early treatment.

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Mitochondrial single nucleotide polymorphism genotyping by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using cleavable biotinylated dideoxynucleotides.

Characterization of mitochondrial DNA (mtDNA) single nucleotide polymorphisms (SNPs) and mutations is crucial for disease diagnosis, which requires accurate and sensitive detection methods and quantification due to mitochondrial heteroplasmy. We report here the characterization of mutations for myoclonic epilepsy with ragged red fibers syndrome using chemically cleavable biotinylated dideoxynucleotides and a mass spectrometry (MS)-based solid phase capture (SPC) single base extension (SBE) assay. The method effectively eliminates unextended primers and primer dimers, and the presence of cleavable linkers between the base and biotin allows efficient desalting and release of the DNA products from solid phase for MS analysis. This approach is capable of high multiplexing, and the use of different length linkers for each of the purines and each of the pyrimidines permits better discrimination of the four bases by MS. Both homoplasmic and heteroplasmic genotypes were accurately determined on different mtDNA samples. The specificity of the method for mtDNA detection was validated by using mitochondrial DNA-negative cells. The sensitivity of the approach permitted detection of less than 5% mtDNA heteroplasmy levels. This indicates that the SPC-SBE approach based on chemically cleavable biotinylated dideoxynucleotides and MS enables rapid, accurate, and sensitive genotyping of mtDNA and has broad applications for genetic analysis.

Emergence and Evolution of Olfactory and Gustatory Symptoms in Patients with COVID-19 in the Outpatient Setting.

The present study aimed at evaluating the prevalence of general and sinonasal symptoms in patients with olfactory symptoms and mild coronavirus disease-2019 (COVID-19) and determining the patterns in emergence and resolution of olfactory/gustatory symptoms relative to general and sinonassal symptoms. This was a prospective cross-sectional study conducted at the outpatient otorhinolaryngology clinic at a COVID-19-designated referral Hospital. We included consecutive patients with new-onset olfactory dysfunction and positive polymerase chain reaction (PCR) assay of COVID-19. We asked the patients to fill in a questionnaire about general and sinonasal symptoms in association with anosmia, hyposmia or hypogeusia, and recorded the time course of the olfactory/gustatory symptoms during 2-weeks of follow-up. 76 patients with average age of 38.5 ± 10.6 years were included. Majority of participants (94.7%) had general or sinonasal symptom. There was anosmia in 60.5% and hyposmia in 39.5%, with sudden onset of olfactory symptoms reported in 63.2% of patients. During the follow-up, 30.3% of patients completely and 44.7% partially recovered from anosmia/hyposmia. Regardless of whether the general or olfactory symptoms appeared initially, the general symptoms resolved first while a degree of olfactory dysfunction persisted during the follow-up. Our study showed that hyposmia and anosmia in mild COVID-19 are frequently associated with general and sinonasal symptoms and tend to persist longer than the general and sinonasal symptoms during the course of the disease.

An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of the Liver.

Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this paper, we show the effectiveness of an enhanced method of quasi-static ultrasound elastography for liver cancer assessment. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The strain refinement algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we use strain images generated by the proposed method with an iterative elasticity reconstruction algorithm. Ultrasound RF data collected from a tissue-mimicking phantom and in-vivo data of a liver cancer patient were used to evaluate the proposed method. Results show that while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates. Clinical Relevance- Improved elasticity images of the liver can aid in achieving more reliable diagnosis of liver cancer.