Force-Sensor-Based Estimation of Needle Tip Deflection in Brachytherapy

A virtual sensor is developed for the online estimation of needle tip deflection during permanent interstitial brachytherapy needle insertion. Permanent interstitial brachytherapy is an effective, minimally invasive, and patient friendly cancer treatment procedure. The deflection of the needles used in the procedure, however, undermines the treatment efficiency and, therefore, needs to be minimized. Any feedback control technique to minimize the needle deflection will require feedback of this quantity, which is not easy to provide. The proposed virtual sensor for needle deflection incorporates a force/torque sensor, mounted at the base of the needle that always remains outside the patient. The measured forces/torques are used by a mathematical model, developed based on mechanical needle properties. The resulting estimation of tip deflection in real time during needle insertion is the main contribution of this paper. The proposed approach solely relies on the measured forces and torques without a need for any other invasive/noninvasive sensing devices. A few mechanical models have been introduced previously regarding the way the forces are composed along the needle during insertion; we will compare our model to those approaches in terms of accuracy. In order to conduct experiments to verify the deflection model, a custom-built, 2-DOF robotic system for needle insertion is developed and discussed. This system is a prototype of an intelligent, hand-held surgical assistant tool that incorporates the virtual sensor proposed in this paper

Needle shape estimation in soft tissue based on partial ultrasound image observation

AbstractWe propose a method to estimate the entire shape of a long exible needle, suitable for a needle insertion assistant robot. This method bases its prediction on only a small segment of a needle, imaged via ultrasound, after insertion. An algorithm is developed that can segment a needle observed partially in ultrasound images and fully in camera images, returning a polynomial representation of the needle shape after RANSAC processing. The polynomial corresponding to the partial needle observation in ultrasound images is used as the input to a needle-tissue interaction model that predicts the entire needle shape. The needle shape predicted by the model is compared to the segmented needle shape based on camera images to validate the proposed approach. The results show that the entire needle shape can be accurately predicted in tissues of varying stiffness based on observation of parts of the needle in an ultrasound image. I

A mechanics-based model for simulation and control of flexible needle insertion in soft tissue

AbstractIn needle-based medical procedures, beveled-tip exible needles are steered inside soft tissue with the aim of reaching pre-dened target locations. The efciency of needle-based interventions depends on accurate control of the needle tip. This paper presents a comprehensive mechanics-based model for simulation of planar needle insertion in soft tissue. The proposed model for needle deection is based on beam theory, works in real-time, and accepts the insertion velocity as an input that can later be used as a control command for needle steering. The model takes into account the effects of tissue deformation, needle-tissue friction, tissue cutting force, and needle bevel angle on needle deection. Using a robot that inserts a exible needle into a phantom tissue, various experiments are conducted to separately identify different subsets of the model parameters. The validity of the proposed model is veried by comparing the simulation results to the empirical data. The results demonstrate the accuracy of the proposed model in predicting the needle tip deection for different insertion velocities. I

Large-Scale Meta-GWAS Reveals Common Genetic Factors Linked to Radiation-Induced Acute Toxicities across Cancers

BACKGROUND: This study was designed to identify common genetic susceptibility and shared genetic variants associated with acute radiation-induced toxicity (RIT) across four cancer types (prostate, head and neck, breast, and lung).METHODS: A GWAS meta-analysis was performed using 19 cohorts including 12,042 patients. Acute standardized total average toxicity (rSTATacute) was modelled using a generalized linear regression model for additive effect of genetic variants adjusted for demographic and clinical covariates. LD score regression estimated shared SNP-based heritability of rSTATacute in all patients and for each cancer type.RESULTS: Shared SNP-based heritability of STATacute among all cancer types was estimated at 10% (se = 0.02), and was higher for prostate (17%, se = 0.07), head and neck (27%, se = 0.09), and breast (16%, se = 0.09) cancers. We identified 130 suggestive associated SNPs with rSTATacute (5.0x10-8&lt;P-value&lt;1.0x10-5) across 25 genomic regions. rs142667902 showed the strongest association (effect allele A; effect size -0.17; P-value=1.7x10-7), which is located near DPPA4, encoding a protein involved in pluripotency in stem cells, which are essential for repair of radiation-induced tissue injury. Gene-set enrichment analysis identified 'RNA splicing via endonucleolytic cleavage and ligation' (P = 5.1 x10-6, Pcorrected =0.079) as the top gene set associated with rSTATacute among all patients. In-silico gene expression analysis showed the genes associated with rSTATacute were statistically significantly up-regulated in skin (not sun exposed Pcorrected=0.004; sun exposed Pcorrected=0.026).CONCLUSIONS: There is shared SNP-based heritability for acute RIT across and within individual cancer sites. Future meta-GWAS among large radiotherapy patient cohorts are worthwhile to identify the common causal variants for acute radiotoxicity across cancer types.</p

The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer Risk and Carriers Share a Common Ancestor.

The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case-control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1-3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

Abstract: It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification