Adaptive Therapy Research Articles

Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.

Vagus nerve stimulation (VNS) is a potential treatment option for gastrointestinal (GI) diseases. The present study aimed to understand the physiological effects of VNS on gastrointestinal (GI) function, which is crucial for developing more effective adaptive closed-loop VNS therapies for GI diseases. Electrogastrography (EGG), which measures gastric electrical activities (GEAs) as a proxy to quantify GI functions, was employed in our investigation. We introduced a recording schema that allowed us to simultaneously induce electrical VNS and record EGG. While this setup created a unique model for studying the effects of VNS on the GI function and provided an excellent testbed for designing advanced neuromodulation therapies, the resulting data was noisy, heterogeneous, and required specialized analysis tools. The current study aimed at formulating a systematic and interpretable approach to quantify the physiological effects of electrical VNS on GEAs in ferrets by using signal processing and machine learning techniques. Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. Our results indicated that the electrophysiological changes induced by VNS were optimally characterized by a distinct set of features for each classification scenario. Additionally, our findings demonstrated that the process of feature selection enhanced classification performance and facilitated representation learning.

Digitally interconnected tertiary old age mental healthcare services and primary healthcare services for seniors in remote areas in Greece: An exportable model?

AbstractBackgroundEven though communities in remote areas across the globe age, old age mental healthcare services are mainly tertiary‐ or secondary hospital‐ based and subsequently not easily accessible by seniors with mental disorders living in such communities.MethodsThe INTegRated InterveNtion of pSychogerIatric Care (INTRINSIC) network embodies a model of digitally interconnected tertiary old age mental healthcare services and primary healthcare services for seniors in remote areas in Greece. It is coordinated by the Division of Old age mental health of the 1st Department of Psychiatry of the National and Kapodistrian University of Athens. INTRINSIC network is built around five pillars: The digital platform, bridging healthcare professionals, service users and communities; The old age mental health risk factor surveillance system, enabling the generation of a map of mental health problems of older adults living in each remote community and the initiation of the adequate treatment; the Modified Problem Adaptation Therapy (M‐PATH) intervention; Hearing and vision assessment and support of service users; and an advocacy body consisting of service users, their care partners and families and active members of the local communities. The initial, six‐month phase of INTRINSIC is funded by the Greek Ministry of Health.Results298 seniors living in remote areas of four different Nomenclature of Territorial Units for Statistics 2 (NUTS2) regions have been enrolled in INTRINSIC. They are on average 71.8 years old and 30.5% of them are males. Approximately 18% and 26% of INSTRINSIC service users suffer from depressive and anxiety symptoms, respectively; 62.3% complained about cognitive decline. Of note, only in 8% of service users the diagnosis of dementia had been established prior their enrolment, while 33.2% were treated with antidepressants.ConclusionBased on amplification of existing resources through the cooperation of tertiary‐ and primary healthcare professionals who share a vision of change, the INTRINSIC model offers a pragmatic opportunity to provide high quality old age healthcare services to seniors living in remote communities. The still ongoing collection and analyses of service users’ data may provide a solid foundation for INTRINSIC feasibility and sustainability

Drug dependence in cancer is exploitable by optimally constructed treatment holidays.

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK-JunB-p21 signalling to enforce drug dependence. We identified the optimal scheduling of 'drug holidays' using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.

Evaluation of feasibility phase of adaptive version of locally made bubble continuous positive airway pressure oxygen therapy for the treatment of COVID-19 positive and negative adults with severe pneumonia and hypoxaemia.

Bubble continuous positive airway pressure (bCPAP) oxygen therapy has been shown to be safe and effective in treating children with severe pneumonia and hypoxaemia in Bangladesh. Due to lack of adequate non-invasive ventilatory support during coronavirus disease 2019 (COVID-19) crisis, we aimed to evaluate whether bCPAP was safe and feasible when adapted for use in adults with similar indications. Adults (18-64 years) with severe pneumonia and moderate hypoxaemia (80 to <90% oxygen saturation (SpO2) in room air) were provided bCPAP via nasal cannula at a flow rate of 10 litres per minute (l/min) oxygen at 10 centimetres (cm) H2O pressure, in two tertiary hospitals in Dhaka, Bangladesh. Qualitative interviews and focus group discussions, using a descriptive phenomenological approach, were performed with patients and staff (n = 39) prior to and after the introduction (n = 12 and n = 27 respectively) to understand the operational challenges to the introduction of bCPAP. We enrolled 30 adults (median age 52, interquartile range (IQR) 40-60 years) with severe pneumonia and hypoxaemia and/or acute respiratory distress syndrome (ARDS) irrespective of coronavirus disease 2019 (COVID-19) test results to receive bCPAP. At baseline mean SpO2 on room air was 87% (±2) which increased to 98% (±2), after initiation of bCPAP. The mean duration of bCPAP oxygen therapy was 14.4 ± 24.8 hours. There were no adverse events of note, and no treatment failure or deaths. Operational challenges to the clinical introduction of bCPAP were lack of functioning pulse oximeters, difficult nasal interface fixation among those wearing nose pin, occasional auto bubbling or lack of bubbling in water-filled plastic bottle, lack of holder for water-filled plastic bottle, rapid turnover of trained clinicians at the hospitals, and limited routine care of patients by hospital clinicians particularly after official hours. If the tertiary hospitals in Bangladesh are supplied with well-functioning good quality pulse oximeters and enhanced training of the doctors and nurses on proper use of adapted version of bCPAP, in treating adults with severe pneumonia and hypoxaemia with or without ARDS, the bCPAP was found to be safe, well tolerated and not associated with treatment failure across all study participants. These observations increase the confidence level of the investigators to consider a future efficacy trial of adaptive bCPAP oxygen therapy compared to WHO standard low flow oxygen therapy in such patients. s Although bCPAP oxygen therapy was found to be safe and feasible in this pilot study, several challenges were identified that need to be taken into account when planning a definitive clinical trial.

Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs.

Myoelectric Model Reference Adaptive Control with Adaptive Kalman Filter for a soft elbow exoskeleton

Rehabilitation and assistance exoskeletons have been widely studied because they allow to provide more effective, intensive, and adaptive therapies; in addition, they can be used to augment the user’s capabilities in order to provide movement assistance. In particular, soft robotic exoskeletons have been researched during the past decade because they allow the device to adapt to the body contours, increasing the user’s comfort. One of the main challenges in the development of soft robotic exoskeletons is the design of controllers that allow intuitive and active control of the device. This work addresses the development of a myoelectric Model Reference Adaptive Controller (MRAC) with an Adaptive Kalman Filter for controlling a cable driven soft elbow exoskeleton. The proposed MRAC controller proved to be suitable for both passive and active control of the soft elbow exoskeleton. The controlled system achieved a Mean Absolute Error (MAE) of approximately 4.5° in passive mode and 10° during active mode. Additionally, the active control mode allowed an average reduction of 34% to 40% in the joint torque RMS when performing dynamic Flexion–Extension movements. The active control mode is based on a surface electromyography (sEMG) joint torque estimation algorithm that achieved an approximate MAE of 1Nm. The proposed MRAC controller proved to be robust enough to adapt to the exoskeleton uncertainties and external disturbances; additionally, the adaptive scheme allowed the system to operate with only two sEMG channels and the measurement of the joint angle, which is estimated by using two Inertial Measurement Units.

A Takagi–Sugeno fuzzy controller for minimizing cancer cells with application to androgen deprivation therapy

Androgen deprivation therapy (ADT) is frequently used to treat prostate cancer which is a widespread disease having a very low survival rate. A prolonged course of ADT can increase toxicity and drug resistance. This study proposes an adaptive therapy combining chemotherapy or immunotherapy with the discontinuation of hormone therapy to overcome these obstacles. The super-twisting sliding mode control (STSMC) algorithm is found to be one of the effective approach as an ADT model for obtaining suitable dosage adaptively. The primary objective is to rapidly reduce the number of cancer cells and the duration of drug exposure. The Takagi–Sugeno fuzzy controller-based active control algorithm is introduced, and it’s performance is compared with the STSMC algorithm. While maintaining global asymptotic stability, the Takagi–Sugeno fuzzy controller reduces the duration of therapy to six months. The controllers are implemented utilizing the linear matrix inequality (LMI) algorithm and the yet another LMI (YALMIP) toolset for MATLAB, and their efficacy is validated utilizing MATLAB and Simulink simulations. This study presents a novel approach to improve prostate cancer treatment outcomes by integrating nonlinear control algorithms and adaptive dosage strategies to reduce treatment duration and minimize drug exposure, thereby improving patient outcomes in prostate cancer management.

Differentiation of BCMA-specific induced pluripotent stem cells into rejuvenated CD8αβ+ T cells targeting multiple myeloma

AbstractA major hurdle in adoptive T-cell therapy is cell exhaustion and failure to maintain antitumor responses. Here, we introduce an induced pluripotent stem cell (iPSC) strategy for reprogramming and revitalizing precursor exhausted B-cell maturation antigen (BCMA)-specific T cells to effectively target multiple myeloma (MM). Heteroclitic BCMA72-80 (YLMFLLRKI)–specific CD8+ memory cytotoxic T lymphocytes (CTL) were epigenetically reprogrammed to a pluripotent state, developed into hematopoietic progenitor cells (CD34+ CD43+/CD14− CD235a−), differentiated into the T-cell lineage and evaluated for their polyfunctional activities against MM. The final T-cell products demonstrated (1) mature CD8αβ+ memory phenotype, (2) high expression of activation or costimulatory molecules (CD38, CD28, and 41BB), (3) no expression of immune checkpoint and senescence markers (CTLA4, PD1, LAG3, and TIM3; CD57), and (4) robust proliferation and polyfunctional immune responses to MM. The BCMA-specific iPSC–T cells possessed a single T-cell receptor clonotype with cognate BCMA peptide recognition and specificity for targeting MM. RNA sequencing analyses revealed distinct genome-wide shifts and a distinctive transcriptional profile in selected iPSC clones, which can develop CD8αβ+ memory T cells. This includes a repertoire of gene regulators promoting T-cell lineage development, memory CTL activation, and immune response regulation (LCK, IL7R, 4-1BB, TRAIL, GZMB, FOXF1, and ITGA1). This study highlights the potential application of iPSC technology to an adaptive T-cell therapy protocol and identifies specific transcriptional patterns that could serve as a biomarker for selection of suitable iPSC clones for the successful development of antigen-specific CD8αβ+ memory T cells to improve the outcome in patients with MM.

A TOPAS model for lens-based proton radiography

Objective. Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging. Approach. The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230–930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according to βγ, the inverse product of particle relativistic velocity and particle momentum. Main results. Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV. Significance. Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.

Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy.

Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations. To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice. Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.

Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy.

Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning. This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer. CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network(cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics. The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2mm) has reached 99.02%, and the WET difference is only 1.28mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model. The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.

Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours.

In cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours that expand using either glandular fission or invasive growth. A two-dimensional, lattice-based model of sites containing sensitive and resistant cells within individual glands is developed to study the evolution of glandular tumour cells under continuous and adaptive therapies. We found that although both growth models benefit from adaptive therapy's ability to prevent recurrence, invasive growth benefits more from it than fission growth. This difference is due to the migration of daughter cells into neighboring glands that is absent in fission but present in invasive growth. The migration resulted in greater mixing of cells, enhancing competition induced by adaptive therapy. By varying the initial spatial spread and location of the resistant cells within the tumour, we found that modifying the conditions within the resistant cells containing glands affect both fission and invasive growth. However, modifying the conditions surrounding these glands affect invasive growth only. Our work reveals the interplay between growth mechanism and tumour topology in modulating the effectiveness of cancer therapy.

Estimating Potential Benefits of Online Adaptive Proton Therapy for Head-and-Neck Cancer: A Retrospective Study

Proton therapy is highly sensitive to anatomical changes and setup variations in head-and-neck (HN) treatments. To address this issue, proton centers often acquire patient CT images weekly to monitor patient anatomical changes during the treatment course and perform offline plan adaptation when needed. However, offline adaptation cannot fully account for daily setup variations or the anatomical changes occurring with high frequency. There are a few groups endeavoring to develop advanced technologies to enable online adaptive proton therapy (APT). However, the necessity of online APT remains controversial, as it is unknown that whether online APT will significantly improve treatment quality and outcomes compared to offline APT. The purpose of this study is to estimate the clinical potential of online APT in the management of HN cancers in relation to the current offline APT. Our retrospective study was conducted with four HN patients (35 fractions per patient), who had been treated with intensity modulated proton therapy and had offline adaptation once or twice during their treatment courses. Synthetic CT (sCT) images were generated from 140 daily CBCT images for us to recalculate the dose of the treatment plan in patient's actual treatment anatomy for each treatment fraction and adapt the plan when warranted. These adaptations were assumed to be performed online before treatment delivery to mimic an online APT course. Accumulative doses were calculated for both courses using the CBCT-based sCT images of every fraction for us to compare the target coverage, organ at risk (OAR) sparing, tumor control probability (TCP) and normal tissue complication probability (NTCP). An in-house script was developed to semi-automate this process in a commercial treatment planning system to facilitate our study. All patients would benefit from online APT to different extents. For the first patient, with OAR doses comparable to the actual offline course, the retrospective online APT course improved dose coverages of the three CTVs from 95.2%, 98.64% and 89.53% to 98.88%, 99.81%, 98.97%, which would lead to a 4.52% improvement in TCP. Similarly, online APT would yield a 2.66% improvement in TCP for the second patient. For the third patient, with comparable CTV dose coverages, the mean doses of right parotid and oral cavity were decreased from 29.52 Gy relative biological effectiveness (RBE) and 41.89 Gy RBE to 22.16 Gy RBE and 34.61 Gy RBE, leading to a reduce of 1.67% and 3.40% in NTCP. The mean dose of right parotid was decreased from 21.71 Gy RBE to 19.37 Gy RBE for the last patient, leading to a reduce of 0.73% in NTCP. Our results showed that online APT could better maintain the treatment plan quality than offline APT for all the four patients, despite their significant anatomical changes. Future investigation will focus on collecting more patient data to obtain statistically significant results and help identify the patients to whom the online APT will be of most benefit.

Does a Dominant Intraprostatic Lesion Boost Require Daily Adaptation when Treated with Stereotactic Online Adaptive MR-Guided Therapy?

Multiple trials have demonstrated a dose-response relationship for radiation therapy in the treatment of localized prostate cancer. Recent data has also demonstrated a benefit with whole gland stereotactic radiation therapy (SBRT) in conjunction with a simultaneous integrated boost to the dominant intraprostatic lesion (DIL). SBRT with a DIL boost can often increase dose to nearby organs at risk such as the rectum and online adaptive MR guided radiation therapy (MGgRT) may offer a dosimetric and toxicity benefit. A prospective database of MRgRT patients was queried for intact prostate cancer patients who received SBRT with a SIB to the DIL. The guideline for adaptation for coverage was to ensure the PTV-prostate coverage at 95% of prescribed dose was greater than 92% or by discretion of the treating physician. Adaptions for organs at risk were made to meet prescription constraints. The number of fractions requiring adaptation to meet organs at risk constraints and/or adequate coverage were reviewed. A total of 26 patients were treated with SBRT with a DIL boost using stereotactic online adaptive MRgRT from 2020 to 2022. 10 of 26 patients were treated for re-irradiation of intact prostate. Out of the 130 fractions delivered, 107 fractions required adaptation (82.3%). 59 fractions were adapted for urethra (45.2%), 48 fractions were adapted for bladder (36.9%), 36 fractions were adapted for rectum (27.7%), 23 fractions were adapted for bladder neck (17.7%), and 19 fractions were adapted for coverage (14.6%). For 53 fractions (40.8 %), adaptation was required for more than one organ at risk. A total of 82.3% of fractions required adaptation for patients treated with SBRT with a DIL boost using stereotactic online adaptive MRgRT. Adaptation occurred most frequently for urethral (45.2%), bladder (36.9%), and rectal constraints (27.7%). Further studies are needed to elucidate if daily adaptive online MRgRT translates to reduced patient toxicity and improved quality of life.

Offline Verification of Daily Adaptive Therapy for Locally Advanced Lung Cancer

Daily adaptive therapy (AdaptT) for locally advanced lung cancer has been an intriguing field with the adoption of cone beam CT (CBCT). We have utilized AdaptT for lung cancer. In this workflow, contours for targets and organs at risk from the original CT simulation dataset are deformed and registered to the CBCT for each fraction. A physicist reviews and edits the deformed contours online without MD presence and a new plan is treated daily. Treating physicians are required to attend 1 fraction per week. We sought to evaluate the accuracy of these contours for targets and OARs in comparison to a thoracic physician contouring offline with the same CBCT dataset. The impetus of this study was to evaluate the model of physics directed contouring and ensure that gross tumor volume (GTV) misses are not occurring during treatment, defined as Planning Target Volumes (PTV's) derived from AdaptT failing to encompass the GTV redrawn offline by the physician. Six consecutive patients were treated from 2021-2022 with AdaptT utilizing processes as described above. One CBCT per week was re-contoured by a physician for 5-6 CBCT's per case. The GTVs, heart, and lungs were redrawn without registration. The physician was blinded to the structures approved for that fraction and from clinical information outside of the CT simulation data. The clinical target volume expansions were 5 mm shaved from normal tissue barriers and a uniform 5 mm expansion to form the PTV. The Dice Similarity Coefficient (DSE), mean surface distance (MSD), 95th percentile Hausdorff distance (95HD) for contours were calculated and the True Positive Fraction (TPF) of the redrawn GTVs within the treated PTVs were calculated. Thirty CBCTs from 6 patients were contoured. The TPF was 1 for each redrawn GTV, indicating each GTV drawn by the physician offline would have been encompassed within the PTV for treatment. The median 95HF for the targets was below 5 mm (Table 1) and the MSD for targets was close to 1.07 mm. The DSC for Heart and Lungs approached 1.0, indicating close similarity. The average time the physician spent on contouring was 9.8 minutes compared to 9.6 minutes with AdaptT. All redrawn GTVs were within the treated PTV and there was close agreement of normal structures. We evaluated the current model employing daily physics overview with weekly physician input and it appears safe and feasible. Further prospective study is needed to standardize comparison of contour quality and validate clinical outcomes of this approach.

Predictive Factors for Response to Adaptive Therapy in Thoracic Stereotactic Ablative Radiotherapy

Online adaptive radiotherapy (ART) has been increasingly adopted for clinical use. However, ART for thoracic malignancies has lagged beyond its implementation for other primary cancers. Efforts are needed to identify optimal patients for ART by finding trends for changes in tumor position, shape, or proximity to OARs are needed. We hypothesized tumor size, histology, pre-RT SUV value, and intrathoracic location could influence how tumors change during cone beam computed tomography (CBCT)-based ART Stereotactic Ablative Radiotherapy (SAbR) for thoracic disease. Data was collected from a prospective registry of patients who received CBCT-ART and SAbR for primary and secondary lung tumors. Dosimetry data was obtained from the simulation planning and the daily adaptive workflow. Central lung tumors were defined as those located within 2 cm of the bronchial tree. Plans were either delivered as per simulation or through the online adaptive workflow delivery (AD). Change in planning tumor volumes (PTV) were calculated between initial and final fractions (ΔPTV). A total of 42 patients with a median age of 67 (range 17-90) and median 8.3 months follow up, treated between June 2021 and December 2022 were included. Most patients had NSCLC or presumed NSCLC (73.85%, 31/42), and most lesions were peripheral (61.9%, 26/42) versus central (31%, 13/42) or apical (7.1%, 3/42). Mean dose and median fractions were 52.5 Gy (SD 8.07) and 5 (range 3-5) while median initial (i) PTV was 31.75 cm3 (IQR 42.3 cm3). On average, ΔPTV decreased by 4.9% (SD 21) and volume shrunk by 5 cm3 (SD 14.5). AD improved per fraction PTV coverage and conformality while esophageal, cardiac, and spinal cord dose were significantly decreased (all p < 0.05), and most fractions were delivered with AD (73.4%, 138/188). AD was aborted most often for small iPTVs. ΔPTV grew >10% for two lesions though their iPTV were < 10 cm3. 12/42 ΔPTV were >10% smaller by the end of RT and corresponded to larger iPTVs. Age, lung primary, metastatic disease, smoking status, and tumor location were not predictive for >10% decrease in ΔPTV. Among 24 biopsy-proven NSCLC ΔPTV was >10% smaller in 6/12 patients (50%) with adenocarcinoma and only in 2/12 (16.7%) with SCC, although this was not significant on χ2 testing (p = 0.08). There were no differences in local, regional, distant failure or death comparing those with a ΔPTV of >10% vs <10% (all p > 0.1). Comparing pre-treatment PET SUV and tumor response, lower SUVs appear to be associated with more PTV shrinkage, with no significant PTV change plateauing at SUV 20. However, this analysis was limited by the number of patients with high SUV values. CBCT-ART SAbR is associated with improved PTV coverage, target conformality, and reduced OAR dose. Large iPTV and adenocarcinomas were more likely to decrease >10%. High metabolic activity appeared predictive for a lack of significant ΔPTV. Further clinical and radiographic features should be explored to predict response to ART.

Proton Online Adaptation Using Novel Cone-Beam Computed Tomography System

Cone beam computed tomography (CBCT) has been used in clinic frequently to provide quick, online, and handy three-dimensional images. However, due to its significant artifacts and inaccurate Hounsfield values (HU), CBCT based online proton adaptation is infeasible. Recently, a novel in-room imaging solution was developed to allow larger images, better contrast and faster CBCT imaging acquisition. Our work demonstrated for the first time the feasibility of using this novel CBCT images for direct intensity modulated proton planning. Three patients and three CIRS phantoms were scanned using novel imaging technique (CBCTp) for this work. CT curves were acquired by scanning a CIRS electron density phantom. Stopping power ratio was computed using the stoichiometric method. Proton plans were made on treatment simulation CT and further evaluated on CBCTp to compare the differences. The table below shows three patients and three phantoms plan comparisons. Multiple sites and dose levels were studied. The planning target volume (PTV) coverage (D95%) and mean dose difference between simulation CT and CBCTp are 0.8% and 0.3%, correspondingly. Dosimetrically, phantom and patient plans are almost identical between two imaging techniques. Lung patient plan show the largest variation due to patient tumor change and the quality of breathing. The novel and high quality of CBCT is feasible for direct proton planning with accurate CT calibration. It provides a reliable alternative solution for proton online adaptive therapy.

Assessment of Daily Target Motion to Optimize ITV Margins in Online Adaptive Plan for Liver and Pancreatic SBRT on MR-LINAC

Accurate measurement of daily target motion on online plan adaptation is essential for re-optimizing the internal target volume (ITV) margins. In this study, by analyzing 2D cine MR images acquired in a 1.5T MR-Linac, we assessed daily target motion and its inter-fractional variations, which is then used to customize ITV margins for MR-based online adaptive plan of liver and pancreatic stereotactic body radiotherapy (SBRT). Twenty-threepatients, enrolled in this study, underwent MR-guided online adaptive SBRT on 1.5 T MR-Linac: liver (n = 12 without compression belt (CB), n = 2 with CB) and pancreas (n = 9 with CB). Target motion in a 2D coronal and sagittal MR images (> 200 cine images to cover multiple breathing cycles), as well as a set of 4D-CT images during the simulation, was analyzed to determine patient specific ITV margins for a reference plan. On every fraction of online adaptive SBRT, the 2D MR images were exported to commercially available software along with a planning MR images: a T1-wighted MR image set for pancreas and or a navigated T2-wighted MR image set for liver. Daily target motion was then measured by overlaying the orthogonal cine image set on the T1- or T2-wighted image set. The ITV margin was adjusted when required prior to online plan optimization RESULTS: Thisstudy successfully assessed the daily target motion and inter-fractional variations in 23 liver or pancreatic cancer patients. We found a discrepancy of daily target motion of orthogonal 2D cine MR images from that of 4D-CT, required to adjust the ITV margin for online plan adaptation. In liver SBRT without CB, 15 out of 45 fractions from 12 patients (33.3 %) went through adjusted ITV margin. The tumor motion was large up to a 2.2 cm along the inferior direction in a patient without CB and thus the patient was treated on a conventional Linac with the active breathing control (ABC) unit for breath-hold. On the other hand, no adjustments were necessary for 2 liver patients with CB. In 9 patients with pancreatic cancers, we found the inter-fractional ITV variations in 3 out of 45 fractions (6.7 %) adjusted the ITV margin. The daily target motion in the pancreatic patients with CB was more stable and relatively smaller than that of the liver patients. Theorthogonal 2D cine MR images are evidently essential for assessing daily target motion to evaluate the inter-fractional variation and achieve daily ITV-customized liver and pancreatic SBRT in the 1.5 T MR-Linac. The customized ITV approach will improve the treatment outcome of the MR-guided online adaptive therapy.

Perivascular nanocarrier aggregation as a drug reservoir augments tumor drug delivery and treatment efficacy

Anticancer nanomedicines must overcome a series of physiological and pathological barriers to reach the site of action, and this often leads to low overall therapeutic efficiency. Herein, we present tumor acidity regulated rapid aggregation of nanocarriers around tumor blood vessels to construct drug reservoirs for on-demand drug delivery. We designed two nanocarriers, one that protonates extremely quickly to expose click reactive groups on its surface and that subsequently reacted with the other noncarrier via click chemistry to rapidly form agglomerates as drug reservoirs. In proof-of-concept experiments, vascular disrupting agent combretastatin A4 (CA4) and hypoxia-activated prodrug PR-104A were loaded into the two nanocarriers, respectively. CA4 was enriched near blood vessels while PR-104A diffused deep into tumors and was activated by profound hypoxia levels induced by CA4. This combination produced effective adaptive therapy for tumors with different hypoxia levels.

Optimal Treatment Strategy for Cancer Based on Mathematical Modeling and Impulse Control Theory

Adaptive therapy is a new type of cancer treatment in which time and dose are dynamically changed according to different individuals, which is very different from conventional cancer treatment strategies that use the maximum dose to kill the tumor cells. However, how to determine the time and dose of drug treatment is a challenging problem. In this paper, a competition model between drug-sensitive cells and drug-resistant cells was established, in which pulse intervention was introduced. In addition, based on the theory of pulse optimal control, three pulse optimal control strategies are proposed in the process of cancer treatment by controlling the pulse interval and dose, minimizing the number of tumor cells at the end of the day at minimal cost. Finally, three optimization strategies were compared, using numerical simulation, in terms of tumor burden and the effect on drug-resistant cells. The results show that the hybrid control strategy has the best effect. This work would provide some new ideas for the treatment of cancer.