A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Breast ultrasound lesions recognition::end-to-end deep learning approaches

Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top "mean Dice" score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score >0.5 , 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work

Breast Ultrasound Region of Interest Detection and Lesion Localisation

© 2020 Elsevier B.V. In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods. We propose the use of a deep learning method for breast ultrasound ROI detection and lesion localisation. We use the most accurate object detection deep learning framework – Faster-RCNN with Inception-ResNet-v2 – as our deep learning network. Due to the lack of datasets, we use transfer learning and propose a new 3-channel artificial RGB method to improve the overall performance. We evaluate and compare the performance of our proposed methods on two datasets (namely, Dataset A and Dataset B), i.e. within individual datasets and composite dataset. We report the lesion detection results with two types of analysis: (1) detected point (centre of the segmented region or the detected bounding box) and (2) Intersection over Union (IoU). Our results demonstrate that the proposed methods achieved comparable results on detected point but with notable improvement on IoU. In addition, our proposed 3-channel artificial RGB method improves the recall of Dataset A. Finally, we outline some future directions for the research

Radio-frequency identification (RFID) tag localisation of non-palpable breast lesions a single centre experience

Aim: The purpose of this study is to report the surgical experience and outcomes with pre-operative localisation of non-palpable breast lesions using the RFID tag system. Methods: The cohort for this prospective study included patients over the age of 18 with biopsy proven, non-palpable indeterminate lesions, DCIS or breast cancer requiring pre-operative localisation before surgical excision between September 2020 and July 2022. Results: A total of 312 RFID tags were placed in 299 consecutive patients. Indications for localisation included non-palpable invasive cancer in 255 (85.3%) patients, in situ disease in 38 (12.7%) and indeterminate lesions requiring surgical excision in 6 (2.0%). Both in situ and invasive lesions had a median size of 13 mm (range 4–100 mm) on pre-operative imaging. The RFID tags were in situ for a median time of 21 days before surgery (range 0–233 days). Of the 213 tags, 292 (93.6%) were introduced using ultrasound (USS) guidance and stereotactically in 20 (6.4%). In 3 (1.0%) cases the RFID tag was either not satisfactorily deployed at the intended target or retrieved intra-operatively. Following discussion of post-operative histology by the multi-disciplinary team, further surgery for close or involved margins was for 26 (8.7%) patients. Conclusion: The Hologic RFID tag system can be used for accurate pre-operative localisation of non-palpable masses as well as diffuse abnormalities such as mammographic distortions and calcifications. It has advantages of flexibility for scheduling image-guided insertion independently of scheduled operating lists and can be placed to localise lesions prior to initiating neoadjuvant systemic treatment

Lumbar lordosis and pars interarticularis fractures:a case-control study

The aim of this study is to examine the relationship between lumbar lordosis and pars interarticularis fractures

Improved outcome in penile cancer with radiologically enhanced stratification protocol for lymph node staging procedures: a study in 316 inguinal basins with a mean follow-up of 5 years

Abstract Background Lymph node metastasis is the main determinant of survival in penile cancer patients. Conventionally clinical palpability is used to stratify patients to Inguinal Lymph node dissection (ILND) if clinically node positive (cN +) or Dynamic sentinel node biopsy (DSNB) if clinically node negative (cN0). Studies suggest a false negative rate (FNR) of around 10% (5–13%) for DSNB. To our knowledge there are no studies reporting harder end point of survival and outcomes of all clinically node positive (cN +) patients. We present our outcome data of all patients with penile cancer including false negative rates and survival in both DSNB and ILND groups. Methods One hundred fifty-eight consecutive patients (316 inguinal basins), who had lymph node surgery for penile cancer in a tertiary referral centre from Jan 2008 to 2018, were included in the study. All patients underwent ultrasound (US) ± fine needle aspiration cytology (FNAC) and then MRI/ CT, if needed, to stage their disease. We used combined clinical and radiological criteria (node size, architecture loss, irregular margins) to stratify patients to DSNB vs ILND as opposed to clinical palpability alone. Results 11.2% i.e., 27/241 inguinal basins had lymph node positive disease by DSNB. 54.9% i.e., 39/71 inguinal basins (IBs) had lymph node-positive disease by ILND. 4 inguinal basins with no tracer uptake in sentinel node scans are being monitored at patient’s request and have not had any recurrences to date. With a mean follow-up of 65 months (range 24–150), the false-negative rate (FNR) for DSNB is 0%. Judicious uses of cross-sectional imaging necessitated ILND in 2 inguinal basins with non-palpable nodes and negative US with false positive rate of 6.3% (2/32) for ILND. The same cohort of DSNB patients might have had 11.1% (3/27) FNR if only palpability criteria was used. 43 (28%) patients who did require cross sectional imaging as per our criteria had a low node positive rate of 4.7% (p = 0.03). Mean cancer specific survival of all node-positive patients was 105 months. Conclusion The performance of DSNB improved with enhanced radiological stratification of patients to either DSNB or ILND. We for the first time report the comprehensive outcome of all lymph node staging procedures in penile cancer

Microsatellite instability (MSI-H) is associated with a high immunoscore but not with PD-L1 expression or increased survival in patients (pts.) with metastatic colorectal cancer (mCRC) treated with oxaliplatin (ox) and fluoropyrimidine (FP) with and without bevacizumab (bev): a pooled analysis of the AIO KRK 0207 and RO91 trials

Introduction!#!In a retrospective analysis of two randomized phase III trials in mCRC patients treated first line with oxaliplatin, fluoropyrimidine with and without Bevacizumab (the AIO KRK 0207 and R091 trials) we evaluated the association of high microsatellite instability (MSI-H), immunoscore (IS) and PD-L1 expression in relation to overall survival (OS).!##!Methods!#!In total, 550 samples were analysed. Immunohistochemical analysis of the MMR proteins and additionally fragment length analysis was performed, molecular examinations via allele-discriminating PCR in combination with DNA sequencing. Furthermore PD-L1 and IS were assessed.!##!Results!#!MSI-H tumors were more frequent in right sided tumors (13.66% vs. 4.14%) and were correlated with mutant BRAF (p = 0.0032), but not with KRAS nor NRAS mutations (MT). 3.1% samples were found to be PD-L1 positive, there was no correlation of PDL1 expression with MSI-H status, but in a subgroup analysis of MSI-H tumors the percentage of PD-L1 positive tumors was higher than in MSS tumors (9.75% vs. 2.55%). 8.5% of samples showed a positive IS, MSI-H was associated with a high IS. The mean IS of the pooled population was 0.57 (SD 0.97), while the IS of MSI-H tumors was significantly higher (mean of 2.4; SD 1.4; p =< 0.0001).!##!Discussion!#!Regarding OS in correlation with MSI-H, PD-L1 and IS status we did not find a significant difference. However, PD-L1 positive mCRC tended to exhibit a longer OS compared to PD-L1 negative cancers (28.9 vs. 22.1 months)