The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.

PURPOSE: To evaluate the reproducibility of radiomics features derived via different pre-processing settings from paired T2-weighted imaging (T2WI) prostate lesions acquired within a short interval, to select the setting that yields the highest number of reproducible features, and to evaluate the impact of disease characteristics (i.e., clinical variables) on features reproducibility. MATERIALS AND METHODS: A dataset of 50 patients imaged using T2WI at 2 consecutive examinations was used. The dataset was pre-processed using 48 different settings. A total of 107 radiomics features were extracted from manual delineations of 74 lesions. The inter-scan reproducibility of each feature was measured using the intra-class correlation coefficient (ICC), with ICC values > 0.75 considered good. Statistical differences were assessed using Mann-Whitney U and Kruskal-Wallis tests. RESULTS: The pre-processing parameters strongly influenced the reproducibility of radiomics features of T2WI prostate lesions. The setting that yielded the highest number of features (25 features) with high reproducibility was the relative discretization with a fixed bin number of 64, no signal intensity normalization, and outlier filtering by excluding outliers. Disease characteristics did not significantly impact the reproducibility of radiomics features. CONCLUSION: The reproducibility of T2WI radiomics features was significantly influenced by pre-processing parameters, but not by disease characteristics. The selected pre-processing setting yielded 25 reproducible features.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

OBJECTIVE: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). MATERIALS AND METHODS: T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. RESULTS: AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). DISCUSSION: All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s.

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.

OBJECTIVES: To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. MATERIALS AND METHODS: Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. RESULTS: The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. CONCLUSION: An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.

Relative Enhanced Diffusivity in Prostate Cancer: Protocol Optimization and Diagnostic Potential.

BACKGROUND: Relative enhanced diffusivity (RED) is a potential biomarker for indirectly measuring perfusion in tissue using diffusion-weighted magnetic resonance imaging (MRI) with 3 b values. PURPOSE: To optimize the RED MRI protocol for the prostate, and to investigate its potential for prostate cancer (PCa) diagnosis. STUDY TYPE: Prospective. POPULATION: Ten asymptomatic healthy volunteers and 35 patients with clinical suspicion of PCa. SEQUENCE: 3T T2 - and diffusion-weighted MRI with b values: b = 0, 50, [100], 150, [200], 250, [300], 400, 800 s/mm2 (values in brackets were only used for patients). ASSESSMENT: Monte Carlo simulations were performed to assess noise sensitivity of RED as a function of intermediate b value. Volunteers were scanned 3 times to assess repeatability of RED. Patient data were used to investigate RED's potential for discriminating between biopsy-confirmed cancer and healthy tissue, and between true and false positive radiological findings. STATISTICAL TESTS: Within-subject coefficient of variation (WCV) to assess repeatability and receiver-operating characteristic curve analysis and logistic regression to assess diagnostic performance of RED. RESULTS: The repeatability was acceptable (WCV = 0.2-0.3) for all intermediate b values tested, apart from b = 50 s/mm2 (WCV = 0.3-0.4). The simulated RED values agreed well with the experimental data, showing that an intermediate b value between 150-250 s/mm2 minimizes noise sensitivity in both peripheral zone (PZ) and transition zone (TZ). RED calculated with the b values 0, 150 and 800 s/mm2 was significantly higher in tumors than in healthy tissue in both PZ (P < 0.001, area under the curve [AUC] = 0.85) and PZ + TZ (P < 0.001, AUC = 0.84). RED was shown to aid apparent diffusion coefficient (ADC) in differentiating between false-positive findings and true-positive PCa in the PZ (AUC; RED = 0.71, ADC = 0.74, RED+ADC = 0.77). DATA CONCLUSION: RED is a repeatable biomarker that may have value for prostate cancer diagnosis. An intermediate b value in the range of 150-250 s/mm2 minimizes the influence of noise and maximizes repeatability. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1900-1910.

Objective assessment of cosmetic outcome after targeted intraoperative radiotherapy in breast cancer: results from a randomised controlled trial.

The international randomised targeted intraoperative radiotherapy (TARGIT) trial has demonstrated evidence of non-inferiority between the novel technique of TARGIT (intra-operative radiotherapy with Intrabeam(®)) and conventional external beam radiotherapy (EBRT) in women with early breast cancer in terms of the primary outcome measure of risk of local relapse within the treated breast. Cosmesis is an increasingly important outcome of breast conserving treatment with both surgery and radiotherapy contributing to this. It was unknown if the single high dose of TARGIT may lead to damaging fibrosis and thus impair cosmesis further, so we objectively evaluated the aesthetic outcome of patients within the TARGIT randomised controlled trial. We have used an objective assessment tool for evaluation of cosmetic outcome. Frontal digital photographs were taken at baseline (before TARGIT or EBRT) and yearly thereafter for up to 5 years. The photographs were analysed by BCCT.core, a validated software which produces a composite score based on symmetry, colour and scar. 342 patients were assessed, median age at baseline 64 years (IQR 59-68). The scores were dichotomised into Excellent and Good (EG), and Fair and Poor (FP). There were statistically significant increases in the odds of having an outcome of EG for patients in the TARGIT group relative to the EBRT group at year 1 (OR 2.07, 95 % CI 1.12-3.85, p = 0.021) and year 2 (OR 2.11, 95 % CI 1.0-4.45, p = 0.05). Following a totally objective assessment in a randomised setting, the aesthetic outcome of patients demonstrates that those treated with TARGIT have a superior cosmetic result to those patients who received conventional external beam radiotherapy.

Religious/spiritual coping resources and their relationship with adjustment in patients newly diagnosed with breast cancer in the UK.

BACKGROUND: Religious/spiritual resources may serve multiple functions in adjustment to cancer. However, there is very little evidence of the importance of religious/spiritual variables outside the USA. This paper reports the cross-sectional data of a longitudinal study examining the beneficial and harmful effects of religious/spiritual coping resources on adjustment in the first year after a breast cancer diagnosis. METHOD: One hundred and fifty-five patients newly diagnosed with breast cancer were assessed after surgery. Several aspects of religiousness/spirituality in relation to anxiety and depression were examined: religiosity/spirituality, strength of faith, belief in God, private and public practices, spiritual involvement, perceived spiritual support and positive and negative religious coping strategies. Non-religious coping, social support and optimism were also assessed. RESULTS: 'Feeling punished and abandoned by God' significantly explained 5% of the variance in increased levels of anxiety but was partially mediated by denial coping. It was also partially mediated by acceptance coping, lowering levels of anxiety. Feeling punished and abandoned by God was a significant independent predictor of depressed mood, explaining 4% of the variance. CONCLUSION: Using religious/spiritual resources in the coping process during the early stages of breast cancer may play an important role in the adjustment process in patients with breast cancer. Patients may benefit from having their spiritual needs addressed as experiencing some form of religious/spiritual struggle may serve as a barrier to illness adjustment. Implications for research and clinical practices are discussed.

Label-set impact on deep learning-based prostate segmentation on MRI.

BACKGROUND: Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance. The automatic segmentation method we used was nnU-Net. RESULTS: The selection of training/testing label-set had a significant (p < 0.001) impact on model performance. Furthermore, it was found that model performance was significantly (p < 0.001) higher when the model was trained and tested with the same label-set. Moreover, the results showed that agreement between automatic segmentations was significantly (p < 0.0001) higher than agreement between manual segmentations and that the models were able to outperform the human label-sets used to train them. CONCLUSIONS: We investigated the impact of label-set selection on the performance of a DL-based prostate segmentation model. We found that the use of different sets of manual prostate gland and zone segmentations has a measurable impact on model performance. Nevertheless, DL-based segmentation appeared to have a greater inter-reader agreement than manual segmentation. More thought should be given to the label-set, with a focus on multicenter manual segmentation and agreement on common procedures. CRITICAL RELEVANCE STATEMENT: Label-set selection significantly impacts the performance of a deep learning-based prostate segmentation model. Models using different label-set showed higher agreement than manual segmentations. KEY POINTS: • Label-set selection has a significant impact on the performance of automatic segmentation models. • Deep learning-based models demonstrated true learning rather than simply mimicking the label-set. • Automatic segmentation appears to have a greater inter-reader agreement than manual segmentation.

CROPro: a tool for automated cropping of prostate magnetic resonance images.

Purpose: To bypass manual data preprocessing and optimize deep learning performance, we developed and evaluated CROPro, a tool to standardize automated cropping of prostate magnetic resonance (MR) images. Approach: CROPro enables automatic cropping of MR images regardless of patient health status, image size, prostate volume, or pixel spacing. CROPro can crop foreground pixels from a region of interest (e.g., prostate) with different image sizes, pixel spacing, and sampling strategies. Performance was evaluated in the context of clinically significant prostate cancer (csPCa) classification. Transfer learning was used to train five convolutional neural network (CNN) and five vision transformer (ViT) models using different combinations of cropped image sizes ( 64 × 64 , 128 × 128 , and 256 × 256  pixels2), pixel spacing ( 0.2 × 0.2 , 0.3 × 0.3 , 0.4 × 0.4 , and 0.5 × 0.5 mm 2 ), and sampling strategies (center, random, and stride cropping) over the prostate. T2-weighted MR images ( N = 1475 ) from the online available PI-CAI challenge were used to train ( N = 1033 ), validate ( N = 221 ), and test ( N = 221 ) all models. Results: Among CNNs, SqueezeNet with stride cropping (image size: 128 × 128 , pixel spacing: 0.2 × 0.2 mm 2 ) achieved the best classification performance ( 0.678 ± 0.006 ). Among ViTs, ViT-H/14 with random cropping (image size: 64 × 64 and pixel spacing: 0.5 × 0.5 mm 2 ) achieved the best performance ( 0.756 ± 0.009 ). Model performance depended on the cropped area, with optimal size generally larger with center cropping ( ∼ 40 cm 2 ) than random/stride cropping ( ∼ 10 cm 2 ). Conclusion: We found that csPCa classification performance of CNNs and ViTs depends on the cropping settings. We demonstrated that CROPro is well suited to optimize these settings in a standardized manner, which could improve the overall performance of deep learning models.

Combination of verteporfin-photodynamic therapy with 5-aza-2'-deoxycytidine enhances the anti-tumour immune response in triple negative breast cancer.

Introduction: Triple negative breast cancer (TNBC) is a subtype of breast cancer characterised by its high tumourigenic, invasive, and immunosuppressive nature. Photodynamic therapy (PDT) is a focal therapy that uses light to activate a photosensitizing agent and induce a cytotoxic effect. 5-aza-2'-deoxycytidine (5-ADC) is a clinically approved immunomodulatory chemotherapy agent. The mechanism of the combination therapy using PDT and 5-ADC in evoking an anti-tumour response is not fully understood. Methods: The present study examined whether a single dose of 5-ADC enhances the cytotoxic and anti-tumour immune effect of low dose PDT with verteporfin as the photosensitiser in a TNBC orthotopic syngeneic murine model, using the triple negative murine mammary tumour cell line 4T1. Histopathology analysis, digital pathology and immunohistochemistry of treated tumours and distant sites were assessed. Flow cytometry of splenic and breast tissue was used to identify T cell populations. Bioinformatics were used to identify tumour immune microenvironments related to TNBC patients. Results: Functional experiments showed that PDT was most effective when used in combination with 5-ADC to optimize its efficacy. 5-ADC/PDT combination therapy elicited a synergistic effect in vitro and was significantly more cytotoxic than monotherapies on 4T1 tumour cells. For tumour therapy, all types of treatments demonstrated histopathologically defined margins of necrosis, increased T cell expression in the spleen with absence of metastases or distant tissue destruction. Flow cytometry and digital pathology results showed significant increases in CD8 expressing cells with all treatments, whereas only the 5-ADC/PDT combination therapy showed increase in CD4 expression. Bioinformatics analysis of in silico publicly available TNBC data identified BCL3 and BCL2 as well as the following anti-tumour immune response biomarkers as significantly altered in TNBC compared to other breast cancer subtypes: GZMA, PRF1, CXCL1, CCL2, CCL4, and CCL5. Interestingly, molecular biomarker assays showed increase in anti-tumour response genes after treatment. The results showed concomitant increase in BCL3, with decrease in BCL2 expression in TNBC treatment. In addition, the treatments showed decrease in PRF1, CCL2, CCL4, and CCL5 genes with 5-ADC and 5-ADC/PDT treatment in both spleen and breast tissue, with the latter showing the most decrease. Discussion: To our knowledge, this is the first study that shows which of the innate and adaptive immune biomarkers are activated during PDT related treatment of the TNBC 4T1 mouse models. The results also indicate that some of the immune response biomarkers can be used to monitor the effectiveness of PDT treatment in TNBC murine model warranting further investigation in human subjects.

Epithelial fusion during neural tube morphogenesis.

Adhesion and fusion of epithelial sheets marks the completion of many morphogenetic events during embryogenesis. Neural tube closure involves an epithelial fusion sequence in which the apposing neural folds adhere initially via cellular protrusions, proceed to a more stable union, and subsequently undergo remodeling of the epithelial structures to yield a separate neural tube roof plate and overlying nonneural ectoderm. Cellular protrusions comprise lamellipodia and filopodia, and studies in several different systems emphasize the critical role of RhoGTPases in their regulation. How epithelia establish initial adhesion is poorly understood but, in neurulation, may involve interactions between EphA receptors and their ephrinA ligands. Epithelial remodeling is spatially and temporally correlated with apoptosis in the dorsal neural tube midline, but experimental inhibition of this cell death does not prevent fusion and remodeling. A variety of molecular signaling systems have been implicated in the late events of morphogenesis, but genetic redundancy, for example among the integrins and laminins, makes identification of the critical players challenging. An improved understanding of epithelial fusion can provide insights into normal developmental processes and may also indicate the mode of origin of clinically important birth defects.

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges.

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment).

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.

Population pharmacokinetic modelling of indium-based quantum dot nanoparticles: preclinical in vivo studies.

There is considerable interest in biomedical applications of quantum dot (QD) nanoparticles, in particular their use as imaging agents for diagnostic applications. In order to investigate the in vivo biodistribution and the potential toxicity of quantum dots (QDs), it is crucial to develop pharmacokinetic (PK) models as basis for prediction of QDs exposure profiles over time. Here, we investigated the in vivo biodistribution of novel indium-based QDs in mice for up to three months after intravenous administration and subsequently developed a translational population PK model to scale findings to humans. This evaluation was complemented by a comprehensive overview of the in vivo toxicology of QDs in rats. The QDs were primarily taken up by the liver and spleen and were excreted via hepatobiliary and urinary pathways. A non-linear mixed effects modelling approach was used to describe blood and organ disposition characteristics of QDs using a multi-compartment PK model. The observed blood and tissue exposure to QDs was characterised with an acceptable level of accuracy at short and long-term. Of note is the fast distribution of QDs from blood into liver and spleen in the first 24 h post-injection (half-life of 28 min) followed by a long elimination profile (half-life range: 47-90 days). This is the first study to assess the PK properties of QDs using a population pharmacokinetic approach to analyse in vivo preclinical data. No organ damage was observed following systemic administration of QDs at doses as high as 48 mg/kg at 24 h, 1 week and 5 weeks post-injection. In conjunction with the data arising from the toxicology experiments, PK parameter estimates provide insight into the potential PK properties of QDs in humans, which ultimately allow prediction of their disposition and enable optimisation of the design of first-in-human QDs studies.

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.

Elastic scattering spectroscopy for early detection of breast cancer: partially supervised Bayesian image classification of scanned sentinel lymph nodes.

Sentinel lymph node biopsy is a standard diagnosis procedure to determine whether breast cancer has spread to the lymph glands in the armpit (the axillary nodes). The metastatic status of the sentinel node (the first node in the axillary chain that drains the affected breast) is the determining factor in surgery between conservative lumpectomy and more radical mastectomy including axillary node excision. The traditional assessment of the node requires sample preparation and pathologist interpretation. An automated elastic scattering spectroscopy (ESS) scanning device was constructed to take measurements from the entire cut surface of the excised sentinel node and to produce ESS images for cancer diagnosis. Here, we report on a partially supervised image classification scheme employing a Bayesian multivariate, finite mixture model with a Markov random field (MRF) spatial prior. A reduced dimensional space was applied to represent the scanning data of the node by a statistical image, in which normal, metastatic, and nonnodal-tissue pixels are identified. Our results show that our model enables rapid imaging of lymph nodes. It can be used to recognize nonnodal areas automatically at the same time as diagnosing sentinel node metastases with sensitivity and specificity of 85% and 94%, respectively. ESS images can help surgeons by providing a reliable and rapid intraoperative determination of sentinel nodal metastases in breast cancer.

Selection by parasites in spate conditions in wild Trinidadian guppies (Poecilia reticulata).

Gyrodactylids are ubiquitous fish parasites and yet, with the notable exception of Gyrodactylus salaris, few studies have reported the effect of these parasites on host survival in natural populations. Here, we assess the impact of the parasite load of gyrodactylids (G. turnbulli and G. bullatarudis) on the survival and migration of guppies (Poecilia reticulata) in their natural habitat of the Aripo River in Trinidad. The recapture rate of males declined by 19% with every additional parasite, a remarkably high figure given that the parasite load in this study ranged from zero to 20 worms. In addition, with an increased number of parasites, males were more prone to be recovered downstream. In contrast, no effect of parasitism was observed in females. The mean parasite load sharply declined after a series of flushing events during heavy seasonal downpours. The parasite load varied significantly between fish depending on their location in the river, and the size of the fish explained variation in parasite load between individuals. The present study indicates that tropical gyrodactylid parasites can play an important role in the ecology of natural fish populations, causing intense bouts of natural selection in guppies during heavy rains in the wet season.

High Resolution Microultrasound (µUS) Investigation of the Gastrointestinal (GI) Tract.

High resolution, microultrasound (µUS) scanning of the gastrointestinal (GI) tract has potential as an important transmural imaging modality to aid in diagnosis. Operating at higher frequencies than conventional clinical ultrasound instruments, µUS is capable of providing scanned images of the GI tract with higher resolution. To investigate the use of µUS for this application, a phantom which is cost effective, within ethical guidelines and, most importantly, similar in histology to the human GI tract is necessary. Therefore, a phantom utilizing porcine small bowel tissue has been developed for custom assembled µUS scanning systems. Two such systems, a stepping scanner and a continuous sweep scanner were utilized to repeatedly scan regions of prepared samples of porcine small bowel tissue. The porcine small bowel tissue phantom was perfused with degassed phosphate buffer saline (dPBS) solution through a cannula inserted in its mesenteric vessel to simulate in vivo conditions and achieve better µUS mucosal characterization. The µUS system scans a transducer across the tissue phantom to acquire RF echo data, which is then processed using MATLAB. A B-scan reconstruction produces 2D images with relative echo strength mapped to a color map of the user's choice. The phantom developed also allows for modifications such as the insertion of fiducial markers to detect tissue change over time and simultaneous perfusion and scanning, providing a platform for more detailed research and investigation into µUS scanning of the GI tract.

New approaches in breast cancer management: sentinel node biopsy and intraoperative radiotherapy.

Breast cancer is presenting earlier, and treatment is becoming less invasive. We review two recent changes in the approach to management. Sentinel lymph node biopsy is a minimally invasive technique to identify the first draining lymph node in direct communication with the primary tumor; it enables "selective lymphadenectomy." Axillary lymph node dissection is reserved as a therapeutic procedure only for proven node positive patients. The concept has been validated, the techniques have been optimized, and randomized controlled trials have confirmed lower morbidity without compromising regional control compared with conventional treatment. The procedure is considered by many as the standard of care for staging the axilla in early breast cancer, although several unanswered questions remain. Adequate training and experience in the technique are vital to ensure high sentinel node identification and low false negative rates. Intraoperative radiotherapy is an attractive concept that enables delivery of single fraction radiotherapy in the operating room immediately after resection of the primary tumor. It is convenient for patients and appears effective in pilot studies. Partial breast irradiation to the index quadrant has been practiced for many years in the form o brachytherapy. Trials are under way comparing intraoperative radiotherapy with conventional external bea irradiation. Intra-operative radiotherapy should not be used outside of clinical trials until the results of the current randomized trials are known.

Low-cost surface reconstruction for aesthetic results assessment and prediction in breast cancer surgery.

The high incidence and low mortality of breast cancer surgery has led to an increasing emphasis on the cosmetic outcome of surgical treatment. Advances in aesthetic evaluation, as well as surgical planning and outcome prediction, have been investigated by using geometrically precise 3D modelling of the breast surface prior to surgery and after the procedure. However, existing solutions are based on expensive site specific setups and remain weakly validated. In this paper, we explore the possibility of using low-cost RGBD cameras as an affordable and mobile system for breast surface reconstruction. The methodology relies on sensor calibration, uncertainty-driven point filtering, dense reconstruction and subsequent multi-view joint optimization to diffuse residual pose errors. Results from a phantom study, with ground truth obtained through commercially available scanners, indicate that the approach is promising with RMS errors in order of 2 mm. A clinical study shows the practical applicability of our method and compares favourably to high-end scanning solutions.

The emerging role and implications of sentinel node biopsy in breast carcinoma.

The surgical management of breast cancer has become less invasive in recent years. Accurate lymphatic staging remains crucial for determining adjuvant treatment and prognosis. Sentinel node biopsy enables highly accurate lymphatic staging with minimal morbidity. In this article we give a historical overview, outline the technique, give succinct guidelines for practice based on current evidence, examine recently published work as well as highlight the most important trials in progress. The advent of sentinel node biopsy for the routine treatment of breast carcinoma will have significant implications on the whole team involved in beast cancer care. Training for the entire team needs to be addressed, and preparations made as patients are likely to soon demand this procedure as part of the routine management of their breast carcinoma.