Real-time adaptive planning method for radiotherapy treatment delivery for prostate cancer patients, based on a library of plans accounting for possible anatomy configuration changes

DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy

Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.

Feasibility-guided automated planning for stereotactic treatments of prostate cancer

Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal-Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs.

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

OBJECTIVE

Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position.

KEY FINDINGS

AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent.

CONCLUSION

This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients.

IMPLICATIONS FOR PRACTICE

Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.

Dosimetric benefits of daily treatment plan adaptation for prostate cancer stereotactic body radiotherapy

BACKGROUND

Hypofractionation is increasingly being applied in radiotherapy for prostate cancer, requiring higher accuracy of daily treatment deliveries than in conventional image-guided radiotherapy (IGRT). Different adaptive radiotherapy (ART) strategies were evaluated with regard to dosimetric benefits.

METHODS

Treatments plans for 32 patients were retrospectively generated and analyzed according to the PACE-C trial treatment scheme (40 Gy in 5 fractions). Using a previously trained cycle-generative adversarial network algorithm, synthetic CT (sCT) were generated out of five daily cone-beam CT. Dose calculation on sCT was performed for four different adaptation approaches: IGRT without adaptation, adaptation via segment aperture morphing (SAM) and segment weight optimization (ART1) or additional shape optimization (ART2) as well as a full re-optimization (ART3). Dose distributions were evaluated regarding dose-volume parameters and a penalty score.

RESULTS

Compared to the IGRT approach, the ART1, ART2 and ART3 approaches substantially reduced the V37Gy(bladder) and V36Gy(rectum) from a mean of 7.4cm3 and 2.0cm3 to (5.9cm3, 6.1cm3, 5.2cm3) as well as to (1.4cm3, 1.4cm3, 1.0cm3), respectively. Plan adaptation required on average 2.6 min for the ART1 approach and yielded doses to the rectum being insignificantly different from the ART2 approach. Based on an accumulation over the total patient collective, a penalty score revealed dosimetric violations reduced by 79.2%, 75.7% and 93.2% through adaptation.

CONCLUSION

Treatment plan adaptation was demonstrated to adequately restore relevant dose criteria on a daily basis. While for SAM adaptation approaches dosimetric benefits were realized through ensuring sufficient target coverage, a full re-optimization mainly improved OAR sparing which helps to guide the decision of when to apply which adaptation strategy.

Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study

Background

In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle³ for full planning automation of VMAT prostate cancer treatments.

Material and Methods

Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans.

Results

For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification.

Conclusions

The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.

Determination of the CTV-PTV margin for prostate cancer radiotherapy depending on the prostate gland positioning control method

Objective: The objective of the study was to determine the correct CTV-PTV margin, depending on the method used to verify the PG position. In the study, 3 methods of CBCT image superimposition were assessed as based on the location of the prostate gland (CBCT images), a single gold marker, and pubic symphysis respectively.

Materials and methods: The study group consisted of 30 patients undergoing irradiation therapy at the University Hospital in Zielona Góra. The therapy was delivered using the VMAT (Volumetric Modulated Arc Therapy) protocol. CBCT image-based superimposition (prostate-based alignment) was chosen as the reference method. The uncertainty of the PG positioning method was determined and the margin to be used was determined for the CBCT-based reference method. Then, changes in the position of the prostate gland relative to these determined using the single marker and pubic symphysis-based methods were determined. The CTV-PTV margin was calculated at the root of the sum of the squares for the doubled value of method uncertainty for the CBCT image-based alignment method and the value of the difference between the locations of planned and actual isocenters as determined using the method of interest and the CBCT-based alignment method for which the total number of differences accounted for 95% of all differences.

Results: The CTV-PTV margins to be used when the prostate gland is positioned using the CBCT imaging, single marker, and pubic symphysis-based methods were determined. For the CBCT-based method, the following values were obtained for the Vrt, Lng, and Lat directions respectively: 0.43 cm, 0.48 cm, 0.29 cm. For the single marker-based method, the respective values were 0.7 cm, 0.88 cm, and 0.44 cm whereas for the pubic symphysis-based method these were 0.65 cm, 0.76 cm, and 0.46 cm.

Conclusions: Regardless of the method, the smallest margin values were obtained for the lateral direction, with the CBCT-based method facilitating the smallest margins to be used. The largest margins were obtained using the single marker-based alignment method.

Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives

Purpose

Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy).

Artificial intelligence in interventional radiation therapy

AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians' behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery.

Conclusions

AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient's assistance in IRT.

Adipocytes protect fibroblasts from radiation-induced damage by adiponectin secretion

Prostate and colon cancers are among the most common cancers diagnosed annually, and both often require treatment with radiation therapy. Advancement in radiation delivery techniques has led to highly accurate targeting of tumor and sparing of normal tissue; however, in the pelvic region it is anatomically difficult to avoid off-target radiation exposure to other organs. Chronically the effects of normal urogenital tissue exposure can lead to urinary frequency, urinary incontinence, proctitis, and erectile dysfunction. Most of these symptoms are caused by radiation-induced fibrosis and reduce the quality of life for cancer survivors. We have observed in animal models that the severity of radiation-induced fibrosis in normal tissue correlates to damaged fat reservoirs in the pelvic region. We hypothesize that adipocytes may secrete a factor that prevents the induction of radiation-associated fibrosis in normal tissues. In these studies we show that the adipokine, adiponectin, is secreted by primary mouse adipocytes and protects fibroblasts from radiation-induced cell death, myofibroblast formation, and senescence. Further, we demonstrated that adiponectin does not protect colorectal or prostate cancer cells from radiation-induced death. Thus, we propose that adiponectin, or its downstream pathway, would provide a novel target for adjuvant therapy when treating pelvic cancers with radiation therapy.

Triggered Imaging With Auto Beam Hold and Pre-/Posttreatment CBCT During Prostate SABR: Analysis of Time Efficiency, Target Coverage, and Normal Volume Changes

PURPOSE

Our purpose was to investigate time efficiency and target coverage for prostate stereotactic ablative radiation therapy (SABR) using triggered imaging (TI) and auto beam hold.

METHODS AND MATERIALS

A total of 20 patients were treated with volumetric modulated arc-based SABR. Treatment verification consisted of pre- and post-radiation therapy cone beam computed tomography (CBCT) with gold marker-based TI every 3 seconds. In case of ≥3 mm (deviation limit) displacement, the treatment was interrupted and imaging-based correction was performed. Beam interruptions, intrafractional shifts, and treatment times were recorded. Prostate, rectum, and bladder were delineated on each CBCT. Target coverage was evaluated by comparing the individual prostate delineations with 98% isodose contour volumes (% of the evaluated volumes exceeding the reference). Both inter- and intrafractional changes of bladder and rectal volumes were assessed.

RESULTS

The average overall treatment time (±standard deviation) was 18 ± 11 min, with a radiation delivery time of 6 ± 3 min if no intrafractional CBCT acquisitions were necessary (91% of fractions). On average, 1.2 beam interruptions per fraction were required with 0/1 correction in 71% of the fractions. The mean residual 3-dimensional shift was 1.6 mm, exceeding the deviation limit in 8%. In the case of intrafractional CBCT and/or ≥2 corrections the treatment time dramatically increased. The 98% isodose lines did not encompass the prostate in only 8/180 (4%) evaluations in 6 different patients, leading to a loss of D98 between 0.1%-6% as a worst case scenario. The bladder volumes showed significant increases during treatment (P < .01) while rectal volumes were stable.

CONCLUSIONS

Time efficiency of TI + auto beam hold with 3 mm/3 sec threshold during prostate SABR is comparable with competitive techniques, resulting in minimal 3-dimensional residual errors with maintained target coverage. Technical developments are necessary to further reduce radiation delivery time. Use of CBCT allowed full control of rectal volumes, while bladder volumes showed significant increases over time.

Mathematical Modeling Shows That the Response of a Solid Tumor to Antiangiogenic Therapy Depends on the Type of Growth

It has been hypothesized that solid tumors with invasive type of growth should possess intrinsic resistance to antiangiogenic therapy, which is aimed at cessation of the formation of new blood vessels and subsequent shortage of nutrient inflow to the tumor. In order to investigate this effect, a continuous mathematical model of tumor growth is developed, which considers variables of tumor cells, necrotic tissue, capillaries, and glucose as the crucial nutrient. The model accounts for the intrinsic motility of tumor cells and for the convective motion, arising due to their proliferation, thus allowing considering two types of tumor growth—invasive and compact—as well as their combination. Analytical estimations of tumor growth speed are obtained for compact and invasive tumors. They suggest that antiangiogenic therapy may provide a several times decrease of compact tumor growth speed, but the decrease of growth speed for invasive tumors should be only modest. These estimations are confirmed by numerical simulations, which further allow evaluating the effect of antiangiogenic therapy on tumors with mixed growth type and highlight the non-additive character of the two types of growth.

Comparison between Conventional IMRT Planning and a Novel Real-Time Adaptive Planning Strategy in Hypofractionated Regimes for Prostate Cancer: A Proof-of-Concept Planning Study

In prostate cancer external beam radiation therapy (EBRT), intra-fraction prostate drifts may compromise the treatment efficacy by underdosing the target and/or overdosing the organs at risk. In this study, a recently developed real-time adaptive planning strategy for intensity-modulated radiation therapy (IMRT) for prostate cancer was evaluated in hypofractionated regimes against traditional treatment planning based on a treatment volume margin expansion. The proposed workflow makes use of a “library of plans” corresponding to possible intra-fraction prostate positions. During delivery, at each beam end, the plan prepared for the position of the prostate closest to the current one is selected and the corresponding beam delivered. This adaptive planning strategy was compared with the traditional approach on a clinical prostate cancer case where different prostate shift magnitudes were considered. Five, six and fifteen fraction hypofractionated schemes were considered for each of these scenarios. When shifts larger than the treatment margin were present, using the traditional approach the seminal vesicles were underdosed by 3–4% of the prescribed dose. The adaptive approach instead allowed for correct target dose coverage and lowered the dose on the rectum for each dosimetric endpoint on average by 3–4% in all the fractionation schemes. Standard intensity-modulated radiation therapy planning did not always guarantee a correct dose distribution on the seminal vesicles and the rectum. The adaptive planning strategy proposed resulted insensitive to the intra-fraction prostate drifts, produced a dose distribution in agreement with the dosimetric requirements in every case analysed and significantly lowered the dose on the rectum.