Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

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Purpose: A fast-rotating O-ring dedicated intensity modulated radiotherapy (IMRT)/volumetric modulated arc therapy (VMAT) delivery system, the Halcyon, is delivered by default with a fully preconfigured photon beam model in the treatment planning system (TPS). This work reports on the validation and achieved IMRT/VMAT delivery quality on the system. Methods: Acceptance testing followed the vendor's installation product acceptance and was supplemented with mechanical QA. The dosimetric calibration was performed according to the IAEA TRS-398 code-of-practice, delivering 600 cGy/min at 10 cm depth, a 90 cm source-surface distance, and a 10 9 10 cm² field size. The output factors, multileaf collimator (MLC) transmission and dosimetric leaf gap (DLG) were validated by comparing measurements with the modeled values in the TPS. Validation of IMRT/VMAT was conducted following AAPM reports (MPPG 5.a, TG-119). Next, dose measurements were performed for end-to-end (E2E) checks in heterogeneous anthropomorphic phantoms using radiochromic film in multiple planes and using ionization chambers (IC) point measurements. E2E checks were performed for VMAT (cranial, rectum, spine, and head and neck) and IMRT (lung). Additionally, IROC Houston mailed dosimetry audits were performed for the beam calibration and E2E measurements using a thorax phantom (IMRT) and a head and neck phantom (VMAT). Lastly, extensive patient-specific QA was performed for the first patients of each new indication, 26 in total (n rectum = 2, n spine = 5, n lung = 5, n esophagus = 2, n head and neck = 7, n cranial = 5), treated on the fast-rotating O-ring linac. The patient-specific QA followed the AAPM TG-218 guidelines and comprised of portal dosimetry, ArcCHECK diode array, radiochromic film dosimetry in a MultiCube phantom, and IC point measurements. Results: The measured output factors showed an agreement <1% for fields ≥3 9 3 cm². Field sizes ≤2 9 2 cm² had a difference of <2%. The measured single-layer MLC transmission was 0.42 AE 0.01% and the measured DLG was 0.27 AE 0.22 mm. The AAPM MPPG 5.a measurements were fully compliant with the guideline criteria. Dose differences larger than 2% were found for the PDD at large depths (>25 cm). TG-119's confidence limits were achieved for the VMAT point dose measurements and for both the IMRT and VMAT radiochromic film measurements. The TG-119 confidence limits were not achieved for IMRT point dose measurements in both the target (5.9%) and the avoidance structure (6.4%). All E2E tests had point differences below 2.3% and gamma agreement scores above 90.6%. The IROC beam calibration audit showed agreement of <1%. The IROC lung IMRT audit and head and neck VMAT audit had results compliant with the IROC Houston's credentialing criteria. All IMRT and VMAT plans selected for patient-specific QA were within the action limits suggested by TG-218. Conclusions:The fast-rotating O-ring linac and its preconfigured TPS are compliant with the international commissioning criteria of AAPM MPPG 5.a and AAPM TG-119. E2E measur...

To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modulated radiotherapy (IMRT) of esophageal cancer. Material and methods: Two databases were used: a variable database (VarDB) with 56 clinical cases extracted retrospectively, including user-dependent variability in delineation and planning, different machines and beam configurations; and a homogenized database (HomDB), created to reduce this variability by re-contouring and replanning all patients with a fixed class-solution protocol. Experiment 1 analysed the user-dependent variability, using 26 patients planned with the same machine and beam setup (E26-VarDB versus E26-HomDB). Experiment 2 increased the training set by groups of 10 patients (E16, E26, E36, E46, and E56) for both databases. Model evaluation metrics were the mean absolute error (MAE) for selected dose-volume metrics and the global MAE for all body voxels. Results: For Experiment 1, E26-HomDB reduced the MAE for the considered dose-volume metrics compared to E26-VarDB (e.g. reduction of 0.2 Gy for D95-PTV, 1.2 Gy for Dmean-heart or 3.3% for V5-lungs). For Experiment 2, increasing the database size slightly improved performance for HomDB models (e.g. decrease in global MAE of 0.13 Gy for E56-HomDB versus E26-HomDB), but increased the error for the VarDB models (e.g. increase in global MAE of 0.20 Gy for E56-VarDB versus E26-VarDB). Conclusion:A small database may suffice to obtain good DL prediction performance, provided that homogenous training data is used. Data variability reduces the performance of DL models, which is further pronounced when increasing the training set.

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