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This textbook covers a basis of mathematical algorithm in artificial intelligence and clinical adaptation and contribution of AI in radiotherapy. More experienced practitioners and researchers and members of medical physics communities, such as AAPM, ASTRO, and ESTRO, would find this book extremely useful.

Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.

The book provides a review of concepts and methodologies developed and adopted for quantitative imaging-guided radiation dosimetry calculations in targeted radionuclide. It also provides an overview of model design of anthropomorphic computational models and software packages developed for Monte Carlo-based dosimetry calculations.

Background Intra-fraction motion represents a crucial issue in the era of precise radiotherapy in several settings, including breast irradiation. To date, only few data exist on real-time measured intra-fraction motion in breast cancer patients. Continuous surface imaging using visible light offers the capability to monitor patient movements in three-dimensional space without any additional radiation exposure. The aim of the present study was to quantify the uncertainties of possible intra-fractional motion during breast radiotherapy. Material and methods One hundred and four consecutive patients that underwent postoperative radiotherapy following breast conserving surgery or mastectomy were prospectively evaluated during 2028 treatment sessions. During each treatment session the patients’ motion was continuously measured using the Catalyst™ optical surface scanner (C-RAD AB, Sweden) and compared to a reference scan acquired at the beginning of each session. The Catalyst system works through an optical surface imaging with light emitting diode (LED) light and reprojection captured by a charge coupled device (CCD) camera, which provide target position control during treatment delivery with a motion detection accuracy of 0.5 mm. For 3D surface reconstruction, the system uses a non-rigid body algorithm to calculate the distance between the surface and the isocentre and using the principle of optical triangulation. Three-dimensional deviations and relative position differences during the whole treatment fraction were calculated by the system and analyzed statistically. Results Overall, the maximum magnitude of the deviation vector showed a mean change of 1.93 mm ± 1.14 mm (standard deviation [SD]) (95%-confidence interval: [0.48–4.65] mm) and a median change of 1.63 mm during dose application (beam-on time only). Along the lateral and longitudinal axis changes were quite similar (0.18 mm ± 1.06 mm vs. 0.17 mm ± 1.32 mm), on the vertical axis the mean change was 0.68 mm ± 1.53 mm. The mean treatment session time was 154 ± 53 (SD) seconds and the mean beam-on time only was 55 ± 16 s. According to Friedman’s test differences in the distributions of the three possible directions (lateral, longitudinal and vertical) were significant ( p  < 0.01), in post-hoc analysis there were no similarities between any two of the three directions. Conclusion The optical surface imaging system is an accurate and easy tool for real-time motion management in breast cancer radiotherapy. Intra-fraction motion was reported within five millimeters in all directions. Thus, intra-fraction motion in our series of 2028 treatment sessions seems to be of minor clinical relevance in postoperative radiotherapy of breast cancer.

IntroductionOne of the most feared side effects of radiotherapy (RT) in the setting of breast cancer (BC) patients is cardiac toxicity. This side effect can jeopardize the quality of life (QoL) of long-term survivors. The impact of modern techniques of RT such as deep inspiration breath hold (DIBH) have dramatically changed this setting. We report and discuss the results of the literature overview of this paper.Materials and methodsLiterature references were obtained with a PubMed query, hand searching, and clinicaltrials.gov.ResultsWe reported and discussed the toxicity of RT and the improvements due to the modern techniques in the setting of BC patients.ConclusionsBC patients often have a long life expectancy, thus the RT should aim at limiting toxicities and at the same time maintaining the same high cure rates. Further studies are needed to evaluate the risk–benefit ratio to identify patients at higher risk and to tailor the treatment choices.

The aim of the study was to investigate boost volume definition, doses, and delivery techniques for rectal cancer dose intensification. An online survey was made on 25 items (characteristics, simulation, imaging, volumes, doses, planning and treatment). Thirty-eight radiation oncologists joined the study. Twenty-one delivered long-course radiotherapy with dose intensification. Boost volume was delineated on diagnostic magnetic resonance imaging (MRI) in 18 centres (85.7%), and computed tomography (CT) and/or positron emission tomography-CT in 9 (42.8%); 16 centres (76.2%) performed co-registration with CT-simulation. Boost dose was delivered on gross tumor volume in 10 centres (47.6%) and on clinical target volume in 11 (52.4%). The most common total dose was 54-55 Gy (71.4%), with moderate hypofractionation (85.7%). Intensity-modulated radiotherapy (IMRT) was used in all centres, with simultaneous integrated boost in 17 (80.8%) and image-guidance in 18 (85.7%). A high quality of treatment using dose escalation can be inferred by widespread multidisciplinary discussion, MRI-based treatment volume delineation, and radiation delivery relying on IMRT with accurate image-guided radiation therapy protocols.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.

We investigated an electronic portal image device (EPID)-based method to see whether it provides effective and accurate relative dose measurement at abutment leaves in terms of positional errors of the multi-leaf collimator (MLC) leaf position. A Siemens ONCOR machine was used. For the garden fence test, a rectangular field (0.2 × 20 cm) was sequentially irradiated 11 times at 2-cm intervals. Deviations from planned leaf positions were calculated. For the nongap test, relative doses at the MLC abutment region were evaluated by sequential irradiation of a rectangular field (2 × 20 cm) 10 times with a MLC separation of 2 cm without a leaf gap. The integral signal in a region of interest was set to position A (between leaves) and B (neighbor of A). A pixel value at position B was used as background and the pixel ratio (A/B × 100) was calculated. Both tests were performed at four gantry angles (0, 90, 180 and 270°) four times over 1 month. For the nongap test the difference in pixel ratio between the first and last period was calculated. Regarding results, average deviations from planned positions with the garden fence test were within 0.5 mm at all gantry angles, and at gantry angles of 90 and 270° tended to decrease gradually over the month. For the nongap test, pixel ratio tended to increase gradually in all leaves, leading to a decrease in relative doses at abutment regions. This phenomenon was affected by both gravity arising from the gantry angle, and the hardware-associated contraction of field size with this type of machine.

It is important to improve the magnitude of dose variation that is caused by the interplay effect. The aim of this study was to investigate the impact of the number of breaths (NBs) to the dose variation for VMAT‐SBRT to lung cancer. Data on respiratory motion and multileaf collimator (MLC) sequence were collected from the cases of 30 patients who underwent radiotherapy with VMAT‐SBRT for lung cancer. The NBs in the total irradiation time with VMAT and the maximum craniocaudal amplitude of the target were calculated. The MLC sequence complexity was evaluated using the modulation complexity score for VMAT (MCSv). Static and dynamic measurements were performed using a cylindrical respiratory motion phantom and a micro ionization chamber. The 1 standard deviation which were obtained from 10 dynamic measurements for each patient were defined as dose variation caused by the interplay effect. The dose distributions were also verified with radiochromic film to detect undesired hot and cold dose spot. Dose measurements were also performed with different NBs in the same plan for 16 patients in 30 patients. The correlations between dose variations and parameters assessed for each treatment plan including NBs, MCSv, the MCSv/amplitude quotient (TMMCSv), and the MCSv/amplitude quotient × NBs product (IVS) were evaluated. Dose variation was decreased with increasing NBs, and NBs of >40 times maintained the dose variation within 3% in 15 cases. The correlation between dose variation and IVS which were considered NBs was shown stronger (R2 = 0.43, P < 0.05) than TMMCSv (R2 = 0.32, P < 0.05). The NBs is an important factor to reduce the dose variation. The patient who breathes >40 times during irradiation of two partial arcs VMAT (i.e., NBs = 16 breaths per minute) may be suitable for VMAT‐SBRT for lung cancer.

Background Recent studies have demonstrated an increase in the necessity of adaptive planning over the course of lung cancer radiation therapy (RT) treatment. In this study, we evaluated intrathoracic changes detected by cone-beam CT (CBCT) in lung cancer patients during RT. Methods and materials A total of 71 lung cancer patients treated with fractionated CBCT-guided RT were evaluated. Intrathoracic changes and plan adaptation priority (AP) scores were compared between small cell lung cancer (SCLC, n  = 13) and non-small cell lung cancer (NSCLC, n  = 58) patients. Results The median cumulative radiation dose administered was 54 Gy (range 30–72 Gy) and the median fraction dose was 1.8 Gy (range 1.8–3.0 Gy). All patients were subjected to a CBCT scan at least weekly (range 1–5/week). We observed intrathoracic changes in 83 % of the patients over the course of RT [58 % (41/71) regression, 17 % (12/71) progression, 20 % (14/71) atelectasis, 25 % (18/71) pleural effusion, 13 % (9/71) infiltrative changes, and 10 % (7/71) anatomical shift]. Nearly half, 45 % (32/71), of the patients had one intrathoracic soft tissue change, 22.5 % (16/71) had two, and three or more changes were observed in 15.5 % (11/71) of the patients. Plan modifications were performed in 60 % (43/71) of the patients. Visual volume reduction did correlate with the number of CBCT scans acquired ( r  = 0.313, p  = 0.046) and with the timing of chemotherapy administration ( r  = 0.385, p  = 0.013). Conclusion Weekly CBCT monitoring provides an adaptation advantage in patients with lung cancer. In this study, the monitoring allowed for plan adaptations due to tumor volume changes and to other anatomical changes.