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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.

The cities of Taxila in Pakistan, and Ai Khanoum in Afghanistan, are emblematic of the post-Alexandrian and post-Mauryan political landscapes of the broader South and Central Asian regions. While both have been thoroughly excavated, there has been little comparison of their material culture. Through the construction of repositories with artefacts of religious nature for both cities and through illustrating maps for the two cities with the distribution of variables such as material or item categorization can serve as a springboard for studies of hybridity in the ancient world. In addition, I hope to build on larger depositories of said resources for other scholars to use in the future and to have a common resource of artefact databases to build upon. The databases can provide research with templates for the two cities and a corpus of their artefacts for future research.

Objective: Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. Methods: In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. Results: Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). Conclusion: These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement: The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.

Abstract Background Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. Methods One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. Results Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). Conclusion Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems.

The Gandhara palettes have been studied in the field of Gandharan art, particularly for their connection to the broader school and its cultural trajectories. A complete repository of the palettes is useful for the study of the artefacts and their relation to Gandharan art. The database seeks to produce a new depository for the palettes as well as more ambiguous artefacts which may have fulfilled similar roles. The dataset described in this paper provides other information such as bibliography, material and estimated chronology which allows for statistical analysis or unpacking correlations of types and chronologies.

Abstract Background Immune ageing is a result of repetitive microbial challenges along with cell intrinsic or systemic changes occurring during ageing. Mice under ‘specific-pathogen-free’ (SPF) conditions are frequently used to assess immune ageing in long-term experiments. However, physiological pathogenic challenges are reduced in SPF mice. The question arises to what extent murine experiments performed under SPF conditions are suited to analyze immune ageing in mice and serve as models for human immune ageing. Our previous comparisons of same aged mice with different microbial exposures, unambiguously identified distinct clusters of immune cells characteristic for numerous previous pathogen encounters in particular in pet shop mice. Results We here performed single cell mass cytometry assessing splenic as secondary and bone marrow as primary lymphoid organ-derived leukocytes isolated from young versus aged SPF mice in order to delineate alterations of the murine hematopoietic system induced during ageing. We then compared immune clusters from young and aged SPF mice to pet shop mice in order to delineate alterations of the murine hematopoietic system induced by physiological pathogenic challenges and those caused by cell intrinsic or systemic changes during ageing. Notably, distinct immune signatures were similarly altered in both pet shop and aged SPF mice in comparison to young SPF mice, including increased frequencies of memory T lymphocytes, effector-cytokine producing T cells, plasma cells and mature NK cells. However, elevated frequencies of CD4+ T cells, total NK cells, granulocytes, pDCs, cDCs and decreased frequencies of naïve B cells were specifically identified only in pet shop mice. In aged SPF mice specifically the frequencies of splenic IgM+ plasma cells, CD8+ T cells and CD4+ CD25+ Treg were increased as compared to pet shop mice and young mice. Conclusions Our study dissects firstly how ageing impacts both innate and adaptive immune cells in primary and secondary lymphoid organs. Secondly, it partly distinguishes murine intrinsic immune ageing alterations from those induced by physiological pathogen challenges highlighting the importance of designing mouse models for their use in preclinical research including vaccines and immunotherapies.

We classically consider the T cell compartment divided into cytotoxic CD8+ T cells and multiple, different helper CD4+ T cell subsets. Here the authors demonstrate that distinct memory CD8+ T cell subsets phenotypically inhabit CD4+ T cell like populations including some with helper-like characteristics.

Radiologists worldwide use mammography as a reliable tool for breast cancer screening. However, mammography assessment is challenging even for well-trained radiologists, leading to a pressing need for Computer Aided Diagnosis (CAD) systems. In this work, a novel technique for the detection and classification of breast Micro-Calcifications (MCs), which are diagnostically significant but difficult to detect findings, is presented. The proposed method is based on the subtraction of temporally sequential mammogram pairs, after pre-processing and image registration, followed by machine-learning. The classification was performed using several features extracted from the subtracted mammograms and selected during training to optimize the accuracy of the results. Six classifiers were tested in a leave-one-patient-out, 4, 5 and 10 fold cross-validation process. This technique was evaluated on a unique dataset, consisting of temporal sequences of mammograms from 80 patients taken between 1 to 6 years apart. The resulting 320 mammograms were reviewed by 2 radiologists who precisely marked each MC location. The accuracy of classifying MCs as benign or suspicious improved from 91.42% without temporal subtraction and an Ensemble of Decision Trees (EDT), to 99.55% with the use of sequential mammograms and Support Vector Machines (SVMs) with leave-one-patient-out validation. The improvement was statistically significant (p-value

An amendment to this paper has been published and can be accessed via the original article.

Metastatic pancreatic adenocarcinoma confers a poor prognosis. Even with recent advances in the treatment of this disease with the introduction of two modestly effective chemotherapy regimens, complete responses are still very rare. Moreover, there are no published data on how to further manage the patients who achieve a sustained remission following treatment. Herein, we report the case of a patient with metastatic pancreatic adenocarcinoma who achieved a complete response lasting for more than three years after receiving induction chemotherapy with FOLFIRINOX followed by maintenance with FOLFIRI.