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High dose-rate brachytherapy presents a promising therapeutic avenue for prostate cancer management, involving the temporary implantation of catheters which deliver radioactive sources to the cancerous site. However, as catheters puncture and penetrate the prostate, tissue deformation is evident which may affect the accuracy and efficiency of the treatment. In this work, a data-driven in silico modelling procedure is proposed to simulate brachytherapy while accounting for prostate biomechanics. Comprehensive magnetic resonance and transrectal ultrasound images acquired prior, during and post brachytherapy are employed for model personalisation, while the therapeutic procedure is simulated via sequential insertion of multiple catheters in the prostate gland. The medical imaging data are also employed for model evaluation, thus, demonstrating the potential of the proposed in silico procedure to be utilised pre- and intra-operatively in the clinical setting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hadjicharalambous, Roussakis, Bourantas, Ioannou, Miller, Doolan, Strouthos, Zamboglou and Vavourakis.)

Purpose: The aim of this study was a retrospective dosimetric comparison of iridium-192 ( 192 Ir) high-dose-rate (HDR) interstitial brachytherapy plans using model-based dose calculation algorithm (MBDCA) following TG-186 recommendations and TG-43 dosimetry protocol for breast, head-and-neck, and lung patient cohorts, with various treatment concepts and prescriptions., Material and Methods: In this study, 59 interstitial 192 Ir HDR brachytherapy cases treated in our center (22 breast, 22 head and neck, and 15 lung) were retrospectively selected and re-calculated with TG-43 dosimetry protocol as well as with Acuros TM BV dose calculation algorithm, with dose to medium option based on computed tomography images. Treatment planning dose volume parameter differences were determined and their significance was assessed., Results: For the breast planning target volume (PTV), TG-43 formalism calculated higher D 90% , V 95% , V 100% , and V 150% values than Acuros TM BV, ranging from 2.2% to 5.4% (mean differences), as it did for the head and neck cases, ranging from 2.5% to 4.7% and for the interstitial lung cases, ranging from 2.2% to 4.4%, showing statistical significance ( p < 0.001). For the skin D 0.1cm3 , D 0.2cm3 , and D 1cm3 , the values were overestimated by TG-43, with a mean absolute differences of 1.4, 1.8, and 2.0 Gy, respectively for the breast, and 1.0 Gy for all DVH statistics for the head and neck cases compared with Acuros TM BV ( p < 0.001). Ipsilateral lung V 5Gy was also higher in TG-43-calculated plans, with a mean difference of 1.0% and 1.1% in the breast and lung implants, respectively. For the chest wall TG-43, the respective overestimation in D 0.1cm3 and D 1cm3 was 0.8 and 0.8 Gy for the breast, and 0.4 and 0.3 Gy for the interstitial lung cases, respectively., Conclusions: The TG-43 algorithm significantly overestimates the dose to PTVs and surrounding organs at risk (OARs) for breast, head and neck, and lung interstitial implants. TG-43 overestimation is in accordance with previous findings for breast and head and neck. To our knowledge, this is also exhibited for Acuros TM BV for the first time in interstitial lung HDR brachytherapy., Competing Interests: The authors report no conflict of interest., (Copyright © 2024 Termedia.)

Purpose/objectives: Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI auto-segmentation contours produced by five commercial vendors against a common dataset., Methods and Materials: The organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients. Comparisons were made using geometric similarity metrics including volumetric and surface Dice similarity coefficient (vDSC and sDSC), Hausdorff distance (HD) and Added Path Length (APL). To assess the time saved, the time taken to manually draw the expert contours, as well as the time to correct the AI contours, were recorded., Results: There are differences in the number of CT contours offered by each AI auto-segmentation solution at the time of the study (Mir 99; MV 143; Rad 83; Ray 67; Ther 86), with all offering contours of some lymph node levels as well as OARs. Averaged across all structures, the median vDSCs were good for all systems and compared favorably with existing literature: Mir 0.82; MV 0.88; Rad 0.86; Ray 0.87; Ther 0.88. All systems offer substantial time savings, ranging between: breast 14-20 mins; head and neck 74-93 mins; lung 20-26 mins; prostate 35-42 mins. The time saved, averaged across all structures, was similar for all systems: Mir 39.8 mins; MV 43.6 mins; Rad 36.6 min; Ray 43.2 mins; Ther 45.2 mins., Conclusions: All five commercial AI auto-segmentation solutions evaluated in this work offer high quality contours in significantly reduced time compared to manual contouring, and could be used to render the radiotherapy workflow more efficient and standardized., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Doolan, Charalambous, Roussakis, Leczynski, Peratikou, Benjamin, Ferentinos, Strouthos, Zamboglou and Karagiannis.)

To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology., (© 2022. The Author(s).)