Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm

Objective. The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned. Therefore, an automated quality assurance framework was developed with the aim to detect in advance aberrant auto-segmentations. Approach. Five organs (prostate, bladder, anorectum, femoral head left and right) were auto-delineated on CT acquisitions for 48 prostate patients by an in-house trained primary DL model. An experienced radiation oncologist assessed the correctness of the model output and categorised the auto-segmentations into two classes whether minor or major adaptations were needed. Subsequently, an independent, secondary DL model was implemented to delineate the same structures as the primary model. Quantitative comparison metrics were calculated using both models' segmentations and used as input features for a machine learning classification model to predict the output quality of the primary model. Main results. For every organ, the approach of independent validation by the secondary model was able to detect primary auto-segmentations that needed major adaptation with high sensitivity (recall = 1) based on the calculated quantitative metrics. The surface DSC and APL were found to be the most indicated parameters in comparison to standard quantitative metrics for the time needed to adapt auto-segmentations. Significance. This proposed method includes a proof of concept for the use of an independent DL segmentation model in combination with a ML classifier to improve time saving during QA of auto-segmentations. The integration of such system into current automatic segmentation pipelines can increase the efficiency of the radiotherapy contouring workflow

Advancing Radiation-Detected Resonance Ionization towards Heavier Elements and More Exotic Nuclides

RAdiation-Detected Resonance Ionization Spectroscopy (RADRIS) is a versatile method for highly sensitive laser spectroscopy studies of the heaviest actinides. Most of these nuclides need to be produced at accelerator facilities in fusion-evaporation reactions and are studied immediately after their production and separation from the primary beam due to their short half-lives and low production rates of only a few atoms per second or less. Only recently, the first laser spectroscopic investigation of nobelium (Z=102) was performed by applying the RADRIS technique in a buffer-gas-filled stopping cell at the GSI in Darmstadt, Germany. To expand this technique to other nobelium isotopes and for the search for atomic levels in the heaviest actinide element, lawrencium (Z=103), the sensitivity of the RADRIS setup needed to be further improved. Therefore, a new movable double-detector setup was developed, which enhances the overall efficiency by approximately 65% compared to the previously used single-detector setup. Further development work was performed to enable the study of longer-lived (t₁/₂>1 h) and shorter-lived nuclides (t₁/₂<1 s) with the RADRIS method. With a new rotatable multi-detector design, the long-lived isotope 254Fm (t₁/₂=3.2 h) becomes within reach for laser spectroscopy. Upcoming experiments will also tackle the short-lived isotope 251No (t₁/₂=0.8 s) by applying a newly implemented short RADRIS measurement cycle

High-resolution laser system for the S3-Low Energy Branch

In this paper we present the first high-resolution laser spectroscopy results obtained at the GISELE laser laboratory of the GANIL-SPIRAL2 facility, in preparation for the first experiments with the S$^3$-Low Energy Branch. Studies of neutron-deficient radioactive isotopes of erbium and tin represent the first physics cases to be studied at S$^3$. The measured isotope-shift and hyperfine structure data are presented for stable isotopes of these elements. The erbium isotopes were studied using the $4f^{12}6s^2$ $^3H_6 \rightarrow 4f^{12}(^3 H)6s6p$ $J = 5$ atomic transition (415 nm) and the tin isotopes were studied by the $5s^25p^2 (^3P_0) \rightarrow 5s^25p6s (^3P_1)$ atomic transition (286.4 nm), and are used as a benchmark of the laser setup. Additionally, the tin isotopes were studied by the $5s^25p6s (^3P_1) \rightarrow 5s^25p6p (^3P_2)$ atomic transition (811.6 nm), for which new isotope-shift data was obtained and the corresponding field-shift $F_{812}$ and mass-shift $M_{812}$ factors are presented

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

Characterizing Prostate Cancer Risk Through Multi-Ancestry Genome-Wide Discovery of 187 Novel Risk Variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Survival outcomes and pattern of relapse after SABR for oligometastatic prostate cancer

INTRODUCTION: The addition of stereotactic ablative radiotherapy (SABR) to standard of care for patients with oligometastatic prostate cancer has the potential of improving survival and delaying further metastases. The primary aim of this analysis is to report survival outcomes and pattern of recurrence of patients with hormone-sensitive (HSPC) and castrate-resistant (CRPC) oligometastatic prostate cancer treated with SABR. METHODS: This is a single-center retrospective study of patients with oligometastatic prostate cancer treated in Iridium Network between 2014 and 2018. All patients with oligometastatic (≤3 active lesions) HSPC and CRPC treated with SABR were included. Data were collected using electronic records. Patterns of first progression following SABR were reported. Kaplan-Meier methods were used to determine survival outcomes. RESULTS: Eighty-seven men received SABR to 115 metastases. Nineteen patients were castrate-resistant and 68 hormone-sensitive at the time of SABR. Median follow-up was 41.6 months. In 25% of patients, no decline from baseline PSA was recorded. Median bPFS was 11.7 months (95% CI 7.6 - 18.3) for HSPC as well as CRPC (95% CI 6.4 - 24.0) (p=0.27). Median DMFS was 21.8 (95% CI 16.9 - 43.2) versus 17.6 months (95% CI 6.7 - 26.2) for HSPC versus CRPC, respectively (p=0.018). Median OS was 72.6 months (95% CI 72.6 – not reached) for HSPC and not reached for CRPC (95% CI 35.4 months – not reached) (p=0.026). For the subgroup of oligorecurrent HSPC, short-term androgen-deprivation therapy was associated with improved bPFS (median 6.0 vs. 18.3 months, HR 0.31, p18 months), 28% (22/79) of patients wmere oligoprogressive (≤3 new lesions) and 27% (21/79) developed a polymetastatic relapse. CONCLUSION: In this cohort, oligometastatic HSPC showed potential benefit from SABR with a median DMFS of 21.8 months. Well-selected patients with oligometastatic CRPC may also benefit from SABR. For patients with metachronous and repeat oligorecurrent HSPC, combining SABR with short-term androgen-deprivation therapy was associated with improved bPFS and DMFS. Overall, 36/87 (41%) of patients were still free from clinical relapse at 18 months

A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer

Background and Purpose: Clinical Artificial Intelligence (AI) implementations lack ground-truth when applied on real-world data. This study investigated how combined geometrical and dose-volume metrics can be used as performance monitoring tools to detect clinically relevant candidates for model retraining. Materials and Methods: Fifty patients were analyzed for both AI-segmentation and planning. For AI-segmentation, geometrical (Standard Surface Dice 3 mm and Local Surface Dice 3 mm) and dose-volume based parameters were calculated for two organs (bladder and anorectum) to compare AI output against the clinically corrected structure. A Local Surface Dice was introduced to detect geometrical changes in the vicinity of the target volumes, while an Absolute Dose Difference (ADD) evaluation increased focus on dose-volume related changes. AI-planning performance was evaluated using clinical goal analysis in combination with volume and target overlap metrics. Results: The Local Surface Dice reported equal or lower values compared to the Standard Surface Dice (anorectum: (0.93 ± 0.11) vs (0.98 ± 0.04); bladder: (0.97 ± 0.06) vs (0.98 ± 0.04)). The ADD metric showed a difference of (0.9 ± 0.8)Gy for the anorectum D1cm3. The bladder D5cm3 reported a difference of (0.7 ± 1.5)Gy. Mandatory clinical goals were fulfilled in 90 % of the DLP plans. Conclusions: Combining dose-volume and geometrical metrics allowed detection of clinically relevant changes, applied to both auto-segmentation and auto-planning output and the Local Surface Dice was more sensitive to local changes compared to the Standard Surface Dice. This monitoring is able to evaluate AI behavior in clinical practice and allows candidate selection for active learning

A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer

Abstract: Background and Purpose: Clinical Artificial Intelligence (AI) implementations lack ground-truth when applied on real-world data. This study investigated how combined geometrical and dose-volume metrics can be used as performance monitoring tools to detect clinically relevant candidates for model retraining.Materials and Methods: Fifty patients were analyzed for both AI-segmentation and planning. For AI-segmentation, geometrical (Standard Surface Dice 3 mm and Local Surface Dice 3 mm) and dose-volume based parameters were calculated for two organs (bladder and anorectum) to compare AI output against the clinically corrected structure. A Local Surface Dice was introduced to detect geometrical changes in the vicinity of the target volumes, while an Absolute Dose Difference (ADD) evaluation increased focus on dose-volume related changes. AI -planning performance was evaluated using clinical goal analysis in combination with volume and target overlap metrics.Results: The Local Surface Dice reported equal or lower values compared to the Standard Surface Dice (anorectum: (0.93 +/- 0.11) vs (0.98 +/- 0.04); bladder: (0.97 +/- 0.06) vs (0.98 +/- 0.04)). The ADD metric showed a difference of (0.9 +/- 0.8)Gy for the anorectum D1cm3. The bladder D5cm3 reported a difference of (0.7 +/- 1.5)Gy. Mandatory clinical goals were fulfilled in 90 % of the DLP plans.Conclusions: Combining dose-volume and geometrical metrics allowed detection of clinically relevant changes, applied to both auto-segmentation and auto-planning output and the Local Surface Dice was more sensitive to local changes compared to the Standard Surface Dice. This monitoring is able to evaluate AI behavior in clinical practice and allows candidate selection for active learning