Here is the design of a clinical trial:

Official Title: The Effect of On-site CT-derived Fractional Flow Reserve on the Management Making for the Patients With Stable Chest Pain (TARGET Trial)

Included conditions:
- Coronary Artery Disease

Study Armgroups:
- {'label': 'CTA/CT-FFR care group', 'type': 'EXPERIMENTAL', 'description': 'If the subjects are randomly allocated to CT-FFR arm, they will be examined by on-site DeepFFR for three major epicardial coronary arteries. If the result of CT-FFR calculation is less than or equal to 0.8 in one or more major coronary arteries, the patient will be referred to ICA directly; if the result of CT-FFR value is more than 0.8, optimal medical therapy will be recommended. The decision on the mode of revascularization is left to the treating cardiologists and depends on local practice standard.', 'interventionNames': ['Diagnostic Test: CT-FFR assessment']}
- {'label': 'Routine clinically-indicated diagnostic care group', 'type': 'NO_INTERVENTION', 'description': 'If the subjects are randomized to usual care arm, attending physicians will decide the next step of diagnosis and treatment, such as exercise ECG, stress cardiac echo, cardiac MR, and SPECT. According to the results of examination combined with risk factors assessment and clinical manifestations, physicians should provide recommendation whether the subjects would undergo ICA or not.'}

Interventions:
- {'type': 'DIAGNOSTIC_TEST', 'name': 'CT-FFR assessment', 'description': 'When subjects are randomized to the CTA/CT-FFR arm, FFR based on the coronary CTA imaging will be measured. DEEPVESSEL FFR workstation is very dedicated software utilizing the original CTA imaging to meter simulated FFR values based on a machine learning algorithm. The first step is to extract a 3D coronary artery model and generate coronary centerlines which are similar to the routine reconstruction of coronary CTA. The centerlines are extracted using a minimal path extraction filter. Then a novel path-based deep learning model, referred to DEEPVESSEL FFR, is used to predict the simulated FFR values on the vascular centerlines. Deep learning algorithm is used to establish characteristic sample database of coronary hemodynamics characteristic parameters. When deep training model is proved to be valid, it is applied to a new lesion-specific measurement. Lesion-specific CT-FFR is defined as simulated FFR value at distance of 20mm away from the lesion of interest.', 'armGroupLabels': ['CTA/CT-FFR care group']}

Primary Outcomes:
- {'measure': 'Number of Participants With ICA Without Obstructive CAD or Intervention', 'description': 'Number of those patients with planned ICA in whom no significant obstructive CAD (no stenosis≥70% by core lab quantitative analysis or invasive FFR≤0.8) is found or interventions (including stent implantation, balloon dilation and bypass graft) are performed during ICA within 90 days.', 'timeFrame': '90 days'}

Please estimate the sample size based on the assumption: 
Significance level is not explicitly mentioned, but the power is assumed to be ≥ 90%. A dropout rate of up to 10% is considered.
Sample size calculation
        The sample size is defined based on the rate of planned ICA without significant obstructive CAD within 90âdays. Based on previous data and assuming the prevalence of non-obstructive CAD during ICA in usual care group is about 30% [7]. The frequency of reduction in the primary endpoint is expected to be 30% for a â¥â90% power. Considering a drop-off up to 10%, the final overall population should be of 1216 patients.