Long-term biopsy outcomes in prostate cancer patients treated with external beam radiotherapy: a systematic review and meta-analysis

Background: Biopsy after external beam radiotherapy (EBRT) for localised prostate cancer (PCa) is an infrequently used but potentially valuable technique to evaluate local recurrence and predict long-term outcomes. Methods: We performed a meta-analysis of studies until March 2020 where a post-EBRT biopsy was performed on patients with low-to intermediate risk PCa, according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. The primary outcome was the aggregate post-EBRT positive biopsy rate (≥2 years after EBRT) and the associated odds ratio (OR) of a positive biopsy on biochemical failure (BCF), distant metastasis-free survival (DMFS) and prostate cancer-specific mortality (PCSM). A sensitivity analysis was performed which examined biopsy rate as a function of post-EBRT biopsy protocol, PCa risk, ADT usage and radiation dose. Results: A total of 22 studies were included, of which 10 were randomised controlled trials and 12 were cohort studies. Nine out of the 22 studies used dosing regimens consistent with the 2020 NCCN radiotherapy guidelines. The weighted-average positive biopsy rate across all 22 studies was 32% (95%-CI: 25–39%, n = 3017). In studies where post-treatment biopsy was part of the study protocol, the rate was 35% (95%-CI: 21–38%, n = 2450). In the subgroup of studies that conformed to the 2020 NCCN radiotherapy guidelines, this rate was 22% (95% CI: 19–41%, n = 832). Patients with positive biopsy had a 10-fold higher odds of developing BCF (OR of 10.3, 95%-CI: 3.7–28.7, p < 0.00001), 3-fold higher odds of developing distant metastasis (OR 3.1, 95%-CI: 2.1–4.7, p < 0.00001) and 5-fold higher odds of dying from their PCa (OR 5.1, 95%-CI: 2.6–10, p < 0.00001). Conclusion: A positive biopsy after EBRT is associated with a poor prognosis compared to a negative biopsy. The post-EBRT positive biopsy rate is an important measure which provides additional insight when comparing EBRT to other treatment modalities for PCa

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly

Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice

Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings

Simultaneous Quantification of Bone Edema/Adiposity and Structure in Inflamed Bone Using Chemical Shift-Encoded MRI in Spondyloarthritis

PURPOSE: To evaluate proton density fat fraction (PDFF) and R2* as markers of bone marrow composition and structure in inflamed bone in patients with spondyloarthritis. METHODS: Phantoms containing fat, water, and trabecular bone were constructed with proton density fat fraction (PDFF) and bone mineral density (BMD) values matching those expected in healthy bone marrow and disease states, and scanned using chemical shift-encoded MRI (CSE-MRI) at 3T. Measured PDFF and R2* values in phantoms were compared with reference FF and BMD values. Eight spondyloarthritis patients and 10 controls underwent CSE-MRI of the sacroiliac joints. PDFF and R2* in areas of inflamed bone and fat metaplasia in patients were compared with normal bone marrow in controls. RESULTS: In phantoms, PDFF measurements were accurate over the full range of PDFF and BMD values. R2* measurements were positively associated with BMD but also were influenced by variations in PDFF. In patients, PDFF was reduced in areas of inflammation and increased in fat metaplasia compared to normal marrow. R2* measurements were significantly reduced in areas of fat metaplasia. CONCLUSION: PDFF measurements reflect changes in marrow composition in areas of active inflammation and structural damage and could be used for disease monitoring in spondyloarthritis. R2* measurements may provide additional information bone mineral density but also are influenced by fat content

Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures

Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: A patient-level classification framework

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these systems are trained using lesion annotations. However, lesion annotations are expensive to obtain and inadequate for characterizing certain tumor types e.g. diffuse tumors and MRI invisible tumors. In this work, we introduce a novel patient-level classification framework, denoted PCF, that is trained using patient-level labels only. In PCF, features are extracted from three-dimensional mpMRI and derived parameter maps using convolutional neural networks and subsequently, combined with clinical features by a multi-classifier support vector machine scheme. The output of PCF is a probability value that indicates whether a patient is harboring clinically significant PCa (Gleason score ≥3+4) or not. PCF achieved mean area under the receiver operating characteristic curves of 0.79 and 0.86 on the PICTURE and PROSTATEx datasets respectively, using five-fold cross-validation. Clinical evaluation over a temporally separated PICTURE dataset cohort demonstrated comparable sensitivity and specificity to an experienced radiologist. We envision PCF finding most utility as a second reader during routine diagnosis or as a triage tool to identify low-risk patients who do not require a clinical read

Improved hepatic arterial fraction estimation using cardiac output correction of arterial input functions for liver DCE MRI

Liver dynamic contrast enhanced (DCE) MRI pharmacokinetic modelling could be useful in the assessment of diffuse liver disease and focal liver lesions, but is compromised by errors in arterial input function (AIF) sampling. In this study, we apply cardiac output correction to arterial input functions (AIFs) for liver dynamic contrast enhanced (DCE) MRI and investigate the effect on dual-input single compartment hepatic perfusion parameter estimation and reproducibility. Thirteen healthy volunteers (28.7±1.94 years, seven males) underwent liver DCE MRI and cardiac output measurement using aortic root phase contrast MRI (PCMRI), with reproducibility (n=9) measured at seven days. Cardiac output AIF correction was undertaken by constraining the first pass AIF enhancement curve using the indicator-dilution principle. Hepatic perfusion parameters with and without cardiac output AIF correction were compared and seven-day reproducibility assessed. Differences between cardiac output corrected and uncorrected liver DCE MRI portal venous (PV) perfusion (p=0.066), total liver blood flow (TLBF)(p=0.101), hepatic arterial (HA) fraction (p=0.895), mean transit time (MTT)(p=0.646), distribution volume (DV)(p=0.890) were not significantly different. Seven-day corrected HA fraction reproducibility was improved (mean difference 0.3%, Bland-Altman 95% Limits-of-Agreement (BA95%LoA) ±27.9%, Coefficient of Variation (CoV) 61.4% vs 9.3%, ±35.5%, 81.7% respectively without correction). Seven-day uncorrected PV perfusion was also improved (mean difference 9.3 ml/min/100g, BA95%LoA ±506.1 ml/min/100g, CoV 64.1% vs 0.9 ml/min/100g, ±562.8 ml/min/100g, 65.1% respectively with correction) as was uncorrected TLBF(mean difference 43.8 ml/min/100g, BA95%LoA ±586.7 ml/min/100g, CoV 58.3% vs 13.3 ml/min/100g, ±661.5 ml/min/100g, 60.9% respectively with correction). Reproducibility of uncorrected MTT was similar (uncorrected mean difference 2.4s, BA95%LoA ±26.7s, CoV 60.8% uncorrected vs 3.7s, ±27.8s, 62.0% respectively with correction), as was and DV (uncorrected mean difference 14.1%, BA95%LoA ±48.2%, CoV 24.7% vs 10.3%, ±46.0%, 23.9% respectively with correction). Cardiac output AIF correction does not significantly affect the estimation of hepatic perfusion parameters but demonstrates improvements in normal volunteer seven-day HA fraction reproducibility, but deterioration in PV perfusion and TLBF reproducibility. Improved HA fraction reproducibility maybe important as arterialisation of liver perfusion is increased in chronic liver disease and within malignant liver lesions

Identification of transcription-factor genes expressed in the Arabidopsis female gametophyte

Dongfang Wang, Changqing Zhang, David J. Hearn, Il-HO Kang, megan I. Skaggs, Karen S. Schumaker, and Ramin Yadegari are with the School of Plant Sciences, University of Arizona, Tucson, Arizona 85721-0036, USA -- Il-Ho Kang, Jayson A. Punwani, and Gary N. Drews are with the Department of Biology, University of Utah, Salt Lake City, Utah 84112-0840, USA -- Changqing Zhang is with The Section of Molecular, Cell and Developmental Biology, University of Texas at Austin, Austin, Texas 78712-0159, USA -- David J. Hearn is with the Department of Biological Sciences, Towson University, Towson, Maryland 21252-0001, USA -- Il-Ho Kang is with the Department of Horticulture, Iowa State University, Ames, Iowa 50011-1100, USA --Jayson A. Punwani is with the Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3280, USABackground In flowering plants, the female gametophyte is typically a seven-celled structure with four cell types: the egg cell, the central cell, the synergid cells, and the antipodal cells. These cells perform essential functions required for double fertilization and early seed development. Differentiation of these distinct cell types likely involves coordinated changes in gene expression regulated by transcription factors. Therefore, understanding female gametophyte cell differentiation and function will require dissection of the gene regulatory networks operating in each of the cell types. These efforts have been hampered because few transcription factor genes expressed in the female gametophyte have been identified. To identify such genes, we undertook a large-scale differential expression screen followed by promoter-fusion analysis to detect transcription-factor genes transcribed in the Arabidopsis female gametophyte. Results Using quantitative reverse-transcriptase PCR, we analyzed 1,482 Arabidopsis transcription-factor genes and identified 26 genes exhibiting reduced mRNA levels in determinate infertile 1 mutant ovaries, which lack female gametophytes, relative to ovaries containing female gametophytes. Spatial patterns of gene transcription within the mature female gametophyte were identified for 17 transcription-factor genes using promoter-fusion analysis. Of these, ten genes were predominantly expressed in a single cell type of the female gametophyte including the egg cell, central cell and the antipodal cells whereas the remaining seven genes were expressed in two or more cell types. After fertilization, 12 genes were transcriptionally active in the developing embryo and/or endosperm. Conclusions We have shown that our quantitative reverse-transcriptase PCR differential-expression screen is sufficiently sensitive to detect transcription-factor genes transcribed in the female gametophyte. Most of the genes identified in this study have not been reported previously as being expressed in the female gametophyte. Therefore, they might represent novel regulators and provide entry points for reverse genetic and molecular approaches to uncover the gene regulatory networks underlying female gametophyte development.Cellular and Molecular [email protected]