Silico Clinical Trials Research Articles

Feasibility of AI-assisted CBCT-guided Stereotactic Online Adaptive Radiotherapy (CT-STAR) for Upper Abdominal SBRT: Results of a Prospective in Silico Clinical Trial

Stereotactic online adaptive radiotherapy (STAR) has been shown to improve the dosimetric therapeutic index (DTI) of SBRT for pancreatic cancer and other upper abdominal malignancies. The feasibility of this technique using a commercially available, online adaptive platform coupled with a ring-gantry Linac and artificial intelligence (AI)-assisted workflows, using cone-beam computed tomography (CBCT) image-guidance, has yet to be evaluated. We conducted a prospective in silico imaging clinical trial of CBCT-guided STAR (CT-STAR), with the hypothesis that CT-STAR would be feasible in silico and would improve the DTI for upper abdominal SBRT. Five patients with upper abdominal malignancies (3 pancreatic, 1 liver, 1 oligometastatic lymph node) who were otherwise undergoing definitive SBRT in 5 fractions (fx) were imaged using high-quality daily CBCT on the ring gantry Linac on each of their clinical treatment days. For all patients, initial plans (50Gy/5fx) were created using clinical planning CT images. CT-STAR was then simulated using the daily CBCT images, comprising all steps of the CT-STAR workflow: image approval, AI-driven autocontouring of influential organs-at-risk (OARs; liver, duodenum, stomach), physician editing of auto-contours, physician editing of the clinical target volume (CTV) and additional OARs (bowel), automated adaptive plan generation and dose volume histogram (DVH) comparison to the initial plan as projected on the daily anatomy, plan approval, pre-delivery quality assurance, and plan delivery. All plans used a strict isotoxicity approach, such that OAR constraints were met at each fx, with PTV coverage increased or decreased as permitted. Feasibility was defined as successful completion of all steps of the CT-STAR process in > 80% of simulated fx. A total of 23 CT-STAR fx were simulated. Median CTV and PTV at baseline were 86.9 cm3 (range, 5.0-115.8 cm3) and 170.9 cm3 (19.4-216.2 cm3). 61 AI OAR contours were autogenerated and reviewed with editing by the physician. AI driven auto-planning led to creation of acceptable plans (OAR constraints met, coverage acceptable) for all fx. In 100% of fx, the adaptive plan was selected over the initial plan, either because the baseline plan initially violated >/ = 1 OAR constraints (15/23fx, 65.2%) or because CTV/PTV coverage was improved by >5% (institutional clinical threshold for adaptation). After all plan re-optimization, the median per-fraction value for the mean PTV dose was 10.61 Gy (9.88-11.33 Gy) and the median max PTV dose was 13.68 Gy (12.51-14.98 Gy). The most common OAR constraint violations prior to adaptation were the duodenum (65.2% fx) and stomach (52.2% fx). 100% of violations were resolved with adaptation. All steps of CT-STAR were successfully completed in 23/23 fx. AI-assisted, CT-STAR is feasible in silico and improves the DTI of SBRT to upper abdominal cancers. Prospective clinical evaluation of this approach is indicated.

Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins*

In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.

Results of a Multicentric In Silico Clinical Trial (ROCOCO): Comparing Radiotherapy with Photons and Protons for Non-small Cell Lung Cancer

This multicentric in silico trial compares photon and proton radiotherapy for non-small cell lung cancer patients. The hypothesis is that proton radiotherapy decreases the dose and the volume of irradiated normal tissues even when escalating to the maximum tolerable dose of one or more of the organs at risk (OAR). Twenty-five patients, stage IA-IIIB, were prospectively included. On 4D F18-labeled fluorodeoxyglucose-positron emission tomography-computed tomography scans, the gross tumor, clinical and planning target volumes, and OAR were delineated. Three-dimensional conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) photon and passive scattered conformal proton therapy (PSPT) plans were created to give 70 Gy to the tumor in 35 fractions. Dose (de-)escalation was performed by rescaling to the maximum tolerable dose. Protons resulted in the lowest dose to the OAR, while keeping the dose to the target at 70 Gy. The integral dose (ID) was higher for 3DCRT (59%) and IMRT (43%) than for PSPT. The mean lung dose reduced from 18.9 Gy for 3DCRT and 16.4 Gy for IMRT to 13.5 Gy for PSPT. For 10 patients, escalation to 87 Gy was possible for all 3 modalities. The mean lung dose and ID were 40 and 65% higher for photons than for protons, respectively. The treatment planning results of the Radiation Oncology Collaborative Comparison trial show a reduction of ID and the dose to the OAR when treating with protons instead of photons, even with dose escalation. This shows that PSPT is able to give a high tumor dose, while keeping the OAR dose lower than with the photon modalities.

Radiation Dose Prescription for Non–Small-Cell Lung Cancer According to Normal Tissue Dose Constraints: An In Silico Clinical Trial

Local tumor recurrence remains a major problem in patients with inoperable non-small-cell lung cancer undergoing radiotherapy. We investigated the theoretical gain in the estimated tumor control probability (TCP) using an individualized maximal tolerable dose (MTD) prescription, for both conventional and accelerated fractionation schemes. For 64 non-small-cell lung cancer patients, five treatment plans were compared, dependent on the normal tissue dose constraints for the lung and spinal cord. The first two used a classic fractionation (2 Gy/d, 5 d/wk) to a total dose of 60 Gy (QD(classic)) or determined by the individualized MTD (QD(MTD)). The third scheme assumed a hypofractionated schedule of 2.75-Gy fractions (QD(hypofr)). The fourth and fifth assumed hyperfractionation and acceleration (1.8 Gy twice daily, either BID(classic) or BID(MTD)). The TCPs for the groups of patients were estimated. The mean biologic equivalent dose in 2-Gy fractions for tumor, corrected for accelerated repopulation was significantly greater for the BID(MTD) scheme (62.1 Gy) than for any other scheme (QD(classic), 47.5 Gy; QD(MTD), 52.0 Gy; QD(hypofr), 56.9 Gy; and BID(classic), 56.9 Gy; p < 0.001). Although both dose-escalation (QD(MTD)) and hypofractionation (QD(hypofr)) resulted in an increase in the mean estimated TCP of 5.6% (p < 0.001) and 14.6% (p < 0.001), respectively, compared with QD(classic), the combination of escalation and acceleration (BID(MTD)) improved the mean estimated TCP by 26.4% (p < 0.001). The results of this planning study showed a large gain in the estimated TCP using an MTD scheme with 1.8-Gy fractions BID compared with other fractionation schedules. Clinical studies implementing this concept are ongoing.