Abstract Background In radiation therapy, Patient‐Specific Quality Assurance (PSQA) is used to ensure safety and accuracy of treatment deliveries, which is paramount to control the disease while safeguarding healthy tissues. In common clinical practice, this involves comparing the planned dose to a dose measured in a phantom prior to the treatment. However, the emergence of Adaptive Radiation Therapy (ART) modalities challenges the PSQA workflow. This is especially the case in online ART workflow, where the patient is maintained immobilized on the treatment couch during treatment adaptation and delivery. Furthermore, for treatment plans using motion management, the plan is adapted in real time to encounter target motion. In these cases, the traditional phantom‐based pre‐treatment PSQA is impractical, and the development of substitute strategies is needed. Purpose To validate a PSQA tool for the Radixact® units, which allows phantomless post‐delivery verifications. Methods In tomotherapy, the dose is optimized by modulating the Leaf Open Times (LOTs) of the Multileaf Collimator (MLC). A metric based on the actual LOTs was developed to verify the accuracy of the dose delivered to the target and to the Organs At Risk (OARs). To validate the metric, treatment plans of prostate and Head and Neck (H&N) patients were modified by introducing errors in the LOT sinogram and subsequently delivered to a phantom. Based on these deliveries, LOT‐based metric indices were calculated and their sensitivity to clinically relevant errors were analyzed and compared to the standard benchmark gamma index metric. The clinically relevant errors included category 1 errors, when measured outputs don't remain within 3% of the established baseline, and category 2 errors, when the clinical goals are not fulfilled. Results The LOT‐based metric outperforms the gamma index metric in predicting dose deviations and clinical goal deviations. It also demonstrates superior performance in classifying acceptable and unacceptable plans in terms of category 1 error detection for H&N patients, while its performance in category 1 error detection for prostate cases and category 2 error detection for both groups is comparable. The LOT‐based metric, with optimal cutoffs, outperforms the gamma index metric in terms of both sensitivity and specificity for category 1 and 2 error detection in prostate cases, while the gamma index metric outperforms the LOT‐based metric in term of sensitivity for category 2 error detection in H&N cases. Conclusions This study establishes the LOT‐based metric as a reliable tool for pretreatment PSQA, serving as a substitute for the conventional gamma index metric, while additionally offering the advantage of fraction‐by‐fraction monitoring of treatment delivery quality.