RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement

Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this study, we propose a deep learning architecture called RADIFUSION that utilizes sequential mammograms and incorporates a linear image attention mechanism, radiomic features, a new gating mechanism to combine different mammographic views, and bilateral asymmetry-based finetuning for breast cancer risk assessment. We evaluate our model on a screening dataset called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on the independent testing set consisting of 1,749 women, our approach achieved superior performance compared to other state-of-the-art models with area under the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866 in the three respective metrics of 1-year AUC, 2-year AUC and > 2-year AUC. Our study highlights the importance of incorporating various deep learning mechanisms, such as image attention, radiomic features, gating mechanism, and bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer risk assessment. We also demonstrate that our model's performance was enhanced by leveraging spatiotemporal information from sequential mammograms. Our findings suggest that RADIFUSION can provide clinicians with a powerful tool for breast cancer risk assessment.Comment: v

Central nervous system intravascular lymphoma in a Malaysian.

Intravascular lymphoma (IVL) is a rare subtype of extranodal diffuse large cell lymphoma, characterized by intravascular proliferation of B or T lymphocytes within small blood vessels; which may lead to occlusive symptoms, its neurological involvement has been said to be uncommon among Asians. 1 We describe a Malaysian with central nervous system IVL, to demonstrate that IVL is an important differential diagnosis in diffuse brain pathology also among Asians

Improved diagnostic accuracy in differentiating malignant and benign lesions using single-voxel proton MRS of the breast at 3 T MRI

AIM: To investigate the diagnostic accuracy of single-voxel proton magnetic resonance spectroscopy (SV (1)H MRS) by quantifying total choline-containing compounds (tCho) in differentiating malignant from benign lesions, and subsequently, to analyse the relationship of tCho levels in malignant breast lesions with their histopathological subtypes. MATERIALS AND METHODS: A prospective study of SV 1H MRS was performed following dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in 61 women using a 3 T MR system. All lesions (n = 57) were analysed for characteristics of morphology, contrast-enhancement kinetics, and tCho peak heights at SV (1)H MRS that were two-times above baseline. Subsequently, the tCho in selected lesions (n = 32) was quantified by calculating the area under the curve, and a tCho concentration equal to or greater than the cut-off value was considered to represent malignancy. The relationship between tCho in invasive ductal carcinomas (IDCs) and their Bloom & Richardson grading of malignancy was assessed. RESULTS: Fifty-two patients (57 lesions; 42 malignant and 15 benign) were analysed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), of predicting malignancy were 100, 73.3, 91.3, and 100%, respectively, using DCE-MRI and 95.2, 93.3, 97.6, and 87.5%, respectively, using SV (1)H MRS. The tCho cut-off for receiver operating characteristic (ROC) curve was 0.33 mmol/l. The relationship between tCho levels in malignant breast lesions with their histopathological subtypes was not statistically significant (p = 0.3). CONCLUSION: Good correlation between tCho peaks and malignancy, enables SV (1)H MRS to be used as a clinically applicable, simple, yet non-invasive tool for improved specificity and diagnostic accuracy in detecting breast cancer

Advanced MRI applications and findings of malignant phyllodes tumour: review of two cases

Phyllodes tumour or cystosarcoma phyllodes is a rare stromal breast tumour that is usually benign but on rare occasions can turn malignant. Non-specificity of the imaging features on sonography and mammography makes it difficult to distinguish malignant from benign counterparts solely based on imaging. The final diagnosis is still highly dependent on histopathological assessment. Herein, we describe two cases of malignant phyllodes tumour with emphasis on magnetic resonance (MR) imaging features using advanced MR applications

Chest Radiograph (CXR) Manifestations of the Novel Coronavirus Disease 2019 (Covid-19) - A Mini-Review

Background Coronavirus disease 2019 (COVID-19) is highly contagious and has claimed more than one million lives, besides causing hardship and disruptions. The Fleischner Society has recommended chest X-ray (CXR) in detecting cases with high risk for disease progression, for triaging suspected patients with moderate-to-severe illness, and to eliminate false negatives in areas with high pre-test probability or limited resources. Although CXR is less sensitive than real-time reverse transcription polymerase chain reaction (RT-PCR) in detecting mild COVID-19, it is nevertheless useful because of equipment portability, low cost and practicality in serial assessments of disease progression among hospitalized patients. Objective This study aims to review the typical and relatively atypical CXR manifestations of COVID-19 pneumonia in a tertiary care hospital. Methods The CXRs of 136 COVID-19 patients confirmed through real-time RT-PCR from March to May 2020 were reviewed. Literature search was performed using PubMed. Results A total of 54 patients had abnormal CXR whilst the others were normal. Typical CXR findings included pulmonary consolidation or ground-glass opacities in a multifocal, bilateral peripheral or lower zone distribution, whereas atypical CXR features comprised cavitation and pleural effusion. Conclusion Typical findings of COVID-19 infection in chest computed tomography studies can also be seen in CXR. The presence of atypical features is associated with worse disease outcome. Recognition of these features on CXR will improve accuracy and speed of diagnosing COVID-19 patients

MRI of Breast Lymphoma: A Report of Two Cases with Emphasis on Diffusion Weighted Imaging and Apparent Diffusion Coefficient Value

Breast lymphoma is a rare neoplasm that accounts for approximately 0.04-0.5% of breast malignancies. Most breast lymphomas are B-cell type non-Hodgkin lymphomas. The imaging features of breast lymphoma on mammography and ultrasound are nonspecific. There have been several reports on magnetic resonance imaging characteristics of breast lymphoma but only few have described features on diffusion weighted imaging. Herein, we describe the magnetic resonance imaging findings, with emphasis on diffusion weighted imaging and the apparent diffusion coefficient sequences, of two cases of breast lymphoma and compare them with the magnetic resonance imaging features reported in the literature

Fusion of B‐mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma

Abstract: In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p‐value, which was established with Student's t‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection

Polygenic risk scores for prediction of breast cancer risk in Asian populations.

PURPOSE: Non-European populations are under-represented in genetics studies, hindering clinical implementation of breast cancer polygenic risk scores (PRSs). We aimed to develop PRSs using the largest available studies of Asian ancestry and to assess the transferability of PRS across ethnic subgroups. METHODS: The development data set comprised 138,309 women from 17 case-control studies. PRSs were generated using a clumping and thresholding method, lasso penalized regression, an Empirical Bayes approach, a Bayesian polygenic prediction approach, or linear combinations of multiple PRSs. These PRSs were evaluated in 89,898 women from 3 prospective studies (1592 incident cases). RESULTS: The best performing PRS (genome-wide set of single-nucleotide variations [formerly single-nucleotide polymorphism]) had a hazard ratio per unit SD of 1.62 (95% CI = 1.46-1.80) and an area under the receiver operating curve of 0.635 (95% CI = 0.622-0.649). Combined Asian and European PRSs (333 single-nucleotide variations) had a hazard ratio per SD of 1.53 (95% CI = 1.37-1.71) and an area under the receiver operating curve of 0.621 (95% CI = 0.608-0.635). The distribution of the latter PRS was different across ethnic subgroups, confirming the importance of population-specific calibration for valid estimation of breast cancer risk. CONCLUSION: PRSs developed in this study, from association data from multiple ancestries, can enhance risk stratification for women of Asian ancestry