Cost-effectiveness of weekly adaptive radiotherapy versus standard IMRT in head and neck cancer alongside the ARTIX trial.

BACKGROUND AND PURPOSE: We performed a cost-effectiveness analysis (CEA) comparing an adaptive radiotherapy (ART) strategy, based on weekly replanning, aiming to correct the parotid gland overdose during treatment and expecting therefore to decrease xerostomia, when compared to a standard IMRT. MATERIALS AND METHODS: We conducted the ARTIX trial, a randomized, parallel-group, multicentric study comparing a systematic weekly replanning ART to a standard IMRT. The primary endpoint was the frequency of xerostomia at 12 months, measured by stimulating salivary flow with paraffin. The CEA was designed alongside the ARTIX trial which was linked to the French national health data system (SNDS). For each patient, healthcare consumptions and costs were provided by the SNDS. The reference case analysis was based on the primary endpoint of the trial. Sensitivity and scenario analyses were performed. RESULTS: Of the 129 patients randomly assigned between 2013 and 2018, only 2 records were not linked to the SNDS, which provides a linkage proportion of 98.4%. All of the other 127 records were linked with good to very good robustness. On the intent-to-treat population at 12 months, mean total costs per patient were €41,564 (SD 23,624) and €33,063 (SD 16,886) for ART and standard IMRT arms, respectively (p = 0.033). Incremental cost effectiveness ratio (ICER) was €162,444 per xerostomia avoided. At 24 months, ICER was €194,521 per xerostomia avoided. For both progression-free and overall survival, ART was dominated by standard IMRT. CONCLUSION: The ART strategy was deemed to be not cost-effective compared with standard IMRT for patients with locally advanced oropharyngeal cancer.

The dosimetric parameters impact on local recurrence in stereotactic radiotherapy for brain metastases.

OBJECTIVES: Stereotactic radiotherapy (SRT) for brain metastases (BM) allows very good local control (LC). However, approximately 20%-30% of these lesions will recur. The objective of this retrospective study was to evaluate the impact of dosimetric parameters on LC in cerebral SRT. METHODS: Patients treated with SRT for 1-3 BM between January 2015 and December 2018 were retrospectively included. A total of 349 patients with 538 lesions were included. The median gross tumour volume (GTV) was 2 cm3 (IQR, 0-7). The median biological effective dose with α/ß = 10 (BED10) was 60 Gy (IQR, 32-82). The median prescription isodose was 71% (IQR, 70-80). Correlations with LC were examined using the Cox regression model. RESULTS: The median follow-up period was 55 months (min-max, 7-85). Median overall survival was 17.8 months (IQR, 15.2-21.9). There were 95 recurrences and LC at 1 and 2 years was 87.1% (95% CI, 84-90) and 78.1% (95% CI, 73.9-82.4), respectively. Univariate analysis showed that systemic treatment, dose to 2% and 50% of the planning target volume (PTV), BED10 > 50 Gy, and low PTV and GTV volume were significantly correlated with better LC. In the multivariate analysis, GTV volume, isodose, and BED10 were significantly associated with LC. CONCLUSION: These results show the importance of a BED10 > 50 Gy associated with a prescription isodose <80% to optimize LC during SRT for BM. ADVANCES IN KNOWLEDGE: Isodose, BED, and GTV volume were significantly associated with LC. A low isodose improves LC without increasing the risk of radionecrosis.

One-year mortality and causes of death after stereotactic radiation therapy for refractory ventricular arrhythmias: A systematic review and pooled analysis.

Patients treated with cardiac stereotactic body radiation therapy (radioablation) for refractory ventricular arrhythmias are patients with advanced structural heart disease and significant comorbidities. However, data regarding 1-year mortality after the procedure are scarce. This systematic review and pooled analysis aimed at determining 1-year mortality after cardiac radioablation for refractory ventricular arrhythmias and investigating leading causes of death in this population. MEDLINE/EMBASE databases were searched up to January 2023 for studies including patients undergoing cardiac radioablation for the treatment of refractory ventricular arrhythmias. Quality of included trials was assessed using the NIH Tool for Case Series Studies (PROSPERO CRD42022379713). A total of 1,151 references were retrieved and evaluated for relevance. Data were extracted from 16 studies, with a total of 157 patients undergoing cardiac radioablation for refractory ventricular arrhythmias. Pooled 1-year mortality was 32 % (95 %CI: 23-41), with almost half of the deaths occurring within three months after treatment. Among the 157 patients, 46 died within the year following cardiac radioablation. Worsening heart failure appeared to be the leading cause of death (52 %), although non-cardiac mortality remained substantial (41 %) in this population. Age≥70yo was associated with a significantly higher 12-month all-cause mortality (p<0.022). Neither target volume size nor radiotherapy device appeared to be associated with 1-year mortality (p = 0.465 and p = 0.199, respectively). About one-third of patients undergoing cardiac stereotactic body radiation therapy for refractory ventricular arrhythmias die within the first year after the procedure. Worsening heart failure appears to be the leading cause of death in this population.

Cardiac stereotactic radiation therapy for refractory ventricular arrhythmias in patients with left ventricular assist devices.

Left ventricular assist device (LVAD) implantation is an established treatment for patients with advanced heart failure refractory to medical therapy. However, the incidence of ventricular arrhythmias (VAs) is high in this population, both in the acute and delayed phases after implantation. About one-third of patients implanted with an LVAD will experience sustained VAs, predisposing these patients to worse outcomes and complicating patient management. The combination of pre-existing myocardial substrate and complex electrical remodeling after LVAD implantation account for the high incidence of VAs observed in this population. LVAD patients presenting VAs refractory to antiarrhythmic therapy and catheter ablation procedures are not rare. In such patients, treatment options are extremely limited. Stereotactic body radiation therapy (SBRT) is a technique that delivers precise and high doses of radiation to highly defined targets, reducing exposure to adjacent normal tissue. Cardiac SBRT has recently emerged as a promising alternative with a growing number of case series reporting the effectiveness of the technique in reducing the VA burden in patients with arrhythmias refractory to conventional therapies. The safety profile of cardiac SBRT also appears favorable, even though the current clinical experience remains limited. The use of cardiac SBRT for the treatment of refractory VAs in patients implanted with an LVAD are even more scarce. This review summarizes the clinical experience of cardiac SBRT in LVAD patients and describes technical considerations related to the implementation of the SBRT procedure in the presence of an LVAD.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy. Materials and methods: In total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D 99% for CTV, V 95% for PTV, Dmax for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models. Results: Considering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D 99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models. Conclusion: The accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use.

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review.

CONTEXT: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases. OBJECTIVE: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC). EVIDENCE ACQUISITION: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool. EVIDENCE SYNTHESIS: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported. CONCLUSIONS: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice. PATIENT SUMMARY: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning.

State of the art and future challenges of urethra-sparing stereotactic body radiotherapy for prostate cancer: a systematic review of literature.

PURPOSE: Doses delivered to the urethra have been associated with an increased risk to develop long-term urinary toxicity in patients undergoing stereotactic body radiotherapy (SBRT) for prostate cancer (PCa). Aim of the present systematic review is to report on the role of urethra-sparing SBRT (US-SBRT) techniques for prostate cancer, with a focus on outcome and urinary toxicity. METHOD: A systematic review of the literature was performed on the PubMed database on May 2023. Based on the urethra-sparing technique, 13 studies were selected for the analysis and classified in the two following categories: "urethra-steering" SBRT (restriction of hotspots to the urethra) and "urethra dose-reduction" SBRT (dose reduction to urethra below the prescribed dose). RESULTS: By limiting the urethra Dmax to 90GyEQD2 (α/ß = 3 Gy) with urethra-steering SBRT techniques, late genitourinary (GU) grade 2 toxicity remains mild, ranging between 12.1% and 14%. With dose-reduction strategies decreasing the urethral dose below 70 GyEQD2, the risk of late GU toxicity was further reduced (< 8% at 5 years), while maintaining biochemical relapse-free survival rates up to 93% at 5 years. CONCLUSION: US-SBRT techniques limiting maximum doses to urethra below a 90GyEQD2 (α/ß = 3 Gy) threshold result in a low rate of acute and late grade ≥ 2 GU toxicity. A better understanding of clinical factors and anatomical substructures involved in the development of GU toxicity, as well as the development and use of adapted dose constraints, is expected to further reduce the long-term GU toxicity of prostate cancer patients treated with SBRT.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background and Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods: Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results: The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions: Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.

Stereotactic body radiation therapy after radical prostatectomy: current status and future directions.

PURPOSE: Around 40% of men with intermediate-risk or high-risk prostate cancer will experience a biochemical recurrence after radical prostatectomy (RP). The aim of this review is to describe both toxicity and oncological outcomes following stereotactic body radiation therapy (SBRT) delivered to the prostate bed (PB). METHOD: In april 2023, we performed a systematic review of studies published in MEDLINE or ClinicalTrials.gov according to Preferred Reporting Items for Systematic Reviews, using the keywords "stereotactic radiotherapy" AND "postoperative" AND "prostate cancer". RESULTS: A total of 14 studies assessing either adjuvant or salvage SBRT to the whole PB or macroscopic local recurrence (MLR) within the PB, and SBRT on radiorecurrent MLR within the PB were included. Doses delivered to either whole PB or MLR between 30 to 40 Gy are associated with a low rate of late grade ≥ 2 genitourinary (GU) toxicity, ranging from 2.2 to 15.1%. Doses above 40 Gy are associated with increased rate of late GU toxicity, raising up to 38%. Oncological outcomes should be interpreted with caution, due to both short follow-up, heterogeneous populations and androgen deprivation therapy (ADT) use. CONCLUSION: PB or MLR SBRT delivered at doses up to 40 Gy appears safe with relatively low late severe GU toxicity rates. Caution is needed with dose-escalated RT schedules above 40 Gy. Further prospective trials are eagerly awaited in this disease setting.

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

Sexual Structure Sparing for Prostate Cancer Radiotherapy: A Systematic Review.

CONTEXT: Erectile dysfunction represents a major side effect of prostate cancer (PCa) treatment, negatively impacting men's quality of life. While radiation therapy (RT) advances have enabled the mitigation of both genitourinary and gastrointestinal toxicities, no significant improvement has been showed in sexual quality of life over time. OBJECTIVE: The primary aim of this review was to assess sexual structures' dose-volume parameters associated with the onset of erectile dysfunction. EVIDENCE ACQUISITION: We searched the PubMed database and ClinicalTrials.gov until January 4, 2023. Studies reporting the impact of the dose delivered to sexual structures on sexual function or the feasibility of innovative sexual structure-sparing approaches were deemed eligible. EVIDENCE SYNTHESIS: Sexual-sparing strategies have involved four sexual organs. The mean penile bulb doses exceeding 20 Gy are predictive of erectile dysfunction in modern PCa RT trial. Maintaining a D100% of ≤36 Gy on the internal pudendal arteries showed preservation of erectile function in 88% of patients at 5 yr. Neurovascular bundle sparing appears feasible with magnetic resonance-guided radiation therapy, yet its clinical impact remains unanswered. Doses delivered to the testicles during PCa RT usually remain <2 Gy and generate a decrease in testosterone levels ranging from -4.6% to -17%, unlikely to have any clinical impact. CONCLUSIONS: Current data highlight the technical feasibility of sexual sparing for PCa RT. The proportion of erectile dysfunction attributable to the dose delivered to sexual structures is still largely unknown. While the ability to maintain sexual function over time is impacted by factors such as age or comorbidities, only selected patients are likely to benefit from sexual-sparing RT. PATIENT SUMMARY: Technical advances in radiation therapy (RT) made it possible to significantly lower the dose delivered to sexual structures. While sexual function is known to decline with age, the preservation of sexual structures for prostate cancer RT is likely to be beneficial only in selected patients.

Weekly Adaptive Radiotherapy vs Standard Intensity-Modulated Radiotherapy for Improving Salivary Function in Patients With Head and Neck Cancer: A Phase 3 Randomized Clinical Trial.

Importance: Xerostomia is a major toxic effect associated with intensity-modulated radiotherapy (IMRT) for oropharyngeal cancers. Objective: To assess whether adaptive radiotherapy (ART) improves salivary function compared with IMRT in patients with head and neck cancer. Design, Setting, and Participants: This phase 3 randomized clinical trial was conducted in 11 French centers. Patients aged 18 to 75 years with stage III-IVB squamous cell oropharyngeal cancer treated with chemoradiotherapy were enrolled between July 5, 2013, and October 1, 2018. Data were analyzed from November 2021 to May 2022. Interventions: The patients were randomly assigned (1:1) to receive standard IMRT (without replanning) or ART (systematic weekly replanning). Main Outcomes and Measures: The primary end point was the frequency of xerostomia, measured by stimulating salivary flow with paraffin. Secondary end points included salivary gland excretory function measured using technetium-99m pertechnetate scintigraphy, patient-reported outcomes (Eisbruch xerostomia-specific questionnaire and the MD Anderson Symptom Inventory for Head and Neck Cancer questionnaire), early and late toxic effects, disease control, and overall and cancer-specific survival. Results: A total of 132 patients were randomized, and after 1 exclusion in the ART arm, 131 were analyzed: 66 in the ART arm (mean [SD] age at inclusion, 60 [8] years; 57 [86.4%] male) and 65 in the standard IMRT arm (mean [SD] age at inclusion, 60 [8] years; 57 [87.7%] male). The median follow-up was 26.4 months (IQR, 1.2-31.3 months). The mean (SD) salivary flow (paraffin) at 12 months was 630 (450) mg/min in the ART arm and 584 (464) mg/min in the standard arm (P = .64). The mean (SD) excretory function of the parotid gland at 12 months, measured by scintigraphy, improved in the ART arm (48% [17%]) compared with the standard arm (41% [17%]) (P = .02). The 2-year-overall survival was 76.9% (95% CI, 64.7%-85.4%) in both arms. Conclusions and Relevance: This randomized clinical trial did not demonstrate a benefit of ART in decreasing xerostomia compared with standard IMRT. No significant differences were found in secondary end points except for parotid gland excretory function, as assessed by scintigraphy, or in survival rates. Trial Registration: ClinicalTrials.gov Identifier: NCT01874587.

Achievable Dosimetric Constraints in Stereotactic Reirradiation for Recurrent Prostate Cancer.

PURPOSE: Stereotactic body radiation therapy has been proposed as a salvage treatment for recurrent prostate cancer after irradiation. One crucial issue is choosing appropriate dose-volume constraints (DVCs) during planning. The objectives of this study were to (1) quantify the proportion of patients respecting the DVCs according to the Urogenital Tumor Study Group GETUG-31 trial, testing 36 Gy in six fractions, (2) explain geometrically why the DVCs could not be respected, and (3) propose the most suitable DVCs. METHODS AND MATERIALS: This retrospective dosimetric analysis included 141 patients treated for recurrent prostate cancer with Cyberknife (Accuray), according to GETUG-31 DVCs: V95% ≥ 95% for the planning target volume (PTV), V12Gy < 20% and V27Gy < 2 cc for the rectum, and V12Gy < 15% and V27Gy < 5 cc for the bladder. The percentage of patients not respecting the DVCs was quantified. Correlations between the DVCs and anatomic structures were examined. New DVCs were proposed. RESULTS: Only 19% of patients respected all DVCs, with a mean PTV of 18.5 cc (range, 3-48 cc), although the mean PTV was 40.5 cc (range, 3-174 cc) in the whole series. A total of 98% of the patients with a clinical target volume (CTV)/prostate ratio >0.5 could not respect the DVCs in the organs at risk. The target coverage and organ-at-risk sparing decreased significantly with increase in the values of PTV, CTV, CTV/prostate ratio, the overlapping volume between the PTV and bladder wall and between the PTV and rectal wall. Threshold values of PTV, >20 cc and 40 cc, allowed for the PTV and bladder DVCs, respectively. To improve DVC respect in case of large target volume, we proposed the following new DVCs: V12Gy < 25% and 25% and V27Gy < 2 cc and 5 cc for the rectum and bladder, respectively. CONCLUSIONS: GETUG-31 DVCs are achievable only for small target volumes (CTV more than half of the prostate). For a larger target volume, new DVCs have been proposed.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

PURPOSE: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients. METHODS AND MATERIALS: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches. The VoxelMorph method was investigated using anatomical scans (VMorph_Sc) or label images (VMorph_Msk), or the combination of both (VMorph_Sc_Msk). Accumulated doses were compared with the planning dose. RESULTS: The DSC ranges, averaged for prostate, rectum and bladder, were 0.60-0.71, 0.67-0.79, 0.93-0.98, and 0.89-0.96 for the FFD, VMorph_Sc, VMorph_Msk, and VMorph_Sc_Msk methods, respectively. When including both anatomical and label images, VoxelMorph estimated more complex deformations resulting in heterogeneous determinant of Jacobian and higher percentage of deformation vector field (DVF) folding (up to a mean value of 1.90% in the prostate). Large differences were observed between DL-based methods regarding estimation of the accumulated dose, showing systematic overdosage and underdosage of the bladder and rectum, respectively. The difference between planned mean dose and accumulated mean dose with VMorph_Sc_Msk reached a median value of +6.3 Gy for the bladder and -5.1 Gy for the rectum. CONCLUSION: The estimation of the deformations using DL-based approach is feasible for male pelvic anatomy but requires the inclusion of anatomical contours to improve organ correspondence. High variability in the estimation of the accumulated dose depending on the deformable strategy suggests further investigation of DL-based techniques before clinical deployment.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.

BACKGROUND: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Salvage Stereotactic Reirradiation for Local Recurrence in the Prostatic Bed After Prostatectomy: A Retrospective Multicenter Study.

BACKGROUND: Management of local recurrence of prostate cancer (PCa) in the prostatic bed after radical prostatectomy (RP) and radiotherapy remains challenging. OBJECTIVE: To assess the efficacy and safety of salvage stereotactic body radiotherapy (SBRT) reirradiation in this setting and evaluate prognostic factors. DESIGN, SETTING, AND PARTICIPANTS: We conducted a large multicenter retrospective series that included 117 patients who were treated with salvage SBRT for local recurrence in the prostatic bed after RP and radiotherapy in 11 centers across three countries. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Progression-free survival (PFS; biochemical, clinical, or both) was estimated using the Kaplan-Meier method. Biochemical recurrence was defined as prostate-specific antigen nadir +0.2 ng/ml, confirmed by a second increasing measure. The cumulative incidence of late toxicities was estimated using the Kalbfleisch-Prentice method by considering recurrence or death as a competing event. RESULTS AND LIMITATIONS: The median follow-up was 19.5 mo. The median SBRT dose was 35 Gy. The median PFS was 23.5 mo (95% confidence interval [95% CI], 17.6-33.2). In the multivariable models, the volume of the recurrence and its contact with the urethrovesical anastomosis were significantly associated with PFS (hazard ratio [HR]/10 cm3 = 1.46; 95% CI, 1.08-1.96; p = 0.01 and HR = 3.35; 95% CI, 1.38-8.16; p = 0.008, respectively). The 3-yr cumulative incidence of grade ≥2 late GU or GI toxicity was 18% (95% CI, 10-26). In the multivariable analysis, a recurrence in contact with the urethrovesical anastomosis and D2% of the bladder were significantly associated with late toxicities of any grade (HR = 3.65; 95% CI, 1.61-8.24; p = 0.002 and HR/10 Gy = 1.88; 95% CI, 1.12-3.16; p = 0.02, respectively). CONCLUSIONS: Salvage SBRT for local recurrence in the prostate bed may offer encouraging control and acceptable toxicity. Therefore, further prospective studies are warranted. PATIENT SUMMARY: We found that salvage stereotactic body radiotherapy after surgery and radiotherapy allows for encouraging control and acceptable toxicity in locally relapsed prostate cancer.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background and purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours. Materials and methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles. Then, the proposed Deep Learning Urethra Segmentation model was trained with the bladder and prostate distance transforms and 44 labeled CT with visible catheters. The evaluation was performed on 11 datasets, calculating centerline distance (CLD) and percentage of centerline within 3.5 and 5 mm. We applied this method to a dataset of 32 patients treated with intensity-modulated radiation therapy (IMRT) to quantify the urethral dose. Finally, we compared predicted intraprostatic urethra contours to manual delineations in MR for 15 patients without catheter. Results: A mean CLD of 1.6 ± 0.8 mm for the whole urethra and 1.7 ± 1.4, 1.5 ± 0.9, and 1.7 ± 0.9 mm for the top, middle, and bottom thirds were obtained in CT. On average, 94% and 97% of the segmented centerlines were within a 3.5 mm and 5 mm radius, respectively. In IMRT, the urethra received a higher dose than the overall prostate. We also found a slight deviation between the predicted and manual MR delineations. Conclusion: A fully-automatic segmentation pipeline was validated to delineate the intraprostatic urethra in CT images.

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma.

BACKGROUND: Predictive tools can be useful for adapting surveillance or including patients in adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current models have been built using traditional statistical modelling and prespecified variables, which limits their performance. OBJECTIVE: To investigate the performance of machine learning (ML) framework to predict recurrence after RCC surgery and compare them with current validated models. DESIGN, SETTING, AND PARTICIPANTS: In this observational study, we derived and tested several ML-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XG boost]) to predict recurrence of patients who underwent radical or partial nephrectomy for a nonmetastatic RCC, between 2013 and 2020, at 21 French medical centres. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary end point was disease-free survival. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using the Brier score. ML models were compared with four conventional prognostic models, using decision curve analysis (DCA). RESULTS AND LIMITATIONS: A total of 4067 patients were included in this study (3253 in the development cohort and 814 in the validation cohort). Most tumours (69%) were clear cell RCC, 40% were of high grade (nuclear International Society of Urological Pathology grade 3 or 4), and 24% had necrosis. Of the patients, 4% had nodal involvement. After a median follow-up of 57 mo (interquartile range 29-76), 523 (13%) patients recurred. ML models obtained higher c-index values than conventional models. The RSF yielded the highest c-index values (0.794), followed by S-SVM (c-index 0.784) and XG boost (c-index 0.782). In addition, all models showed good calibration with low integrated Brier scores (all integrated brier scores <0.1). However, we found calibration drift over time for all models, albeit with a smaller magnitude for ML models. Finally, DCA showed an incremental net benefit from all ML models compared with conventional models currently used in practice. CONCLUSIONS: Applying ML approaches to predict recurrence following surgical resection of RCC resulted in better prediction than that of current validated models available in clinical practice. However, there is still room for improvement, which may come from the integration of novel biological and/or imaging biomarkers. PATIENT SUMMARY: We found that artificial intelligence algorithms could better predict the risk of recurrence after surgery for a localised kidney cancer. These algorithms may help better select patients who will benefit from medical treatment after surgery.