Browsing by Author "Hemmati, Mehdi"

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    Benchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative microsimulation study
    (Elsevier, 2024) Hemmati, Mehdi; Ishizawa, Sayaka; Meza, Rafael; Ostrin, Edwin; Hanash, Samir M.; Antonoff, Mara; Schaefer, Andrew J.; Tammemägi, Martin C.; Toumazis, Iakovos
    Background Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening. Methods In this comparative microsimulation study, we benchmarked alternative clinically relevant structured lung cancer screening programmes employing a fixed (annual or biennial) or adaptive (start with annual/biennial screening and then switch to biennial/annual at ages 60- or 65-years) screening frequency, against the ENGAGE framework. Individuals were eligible for screening according to the 2021 US Preventive Services Task Force recommendation on lung cancer screening. We assessed programmes' efficiency based on the number of screenings per death avoided (LDCT/DA) and the number of screenings per ever-screened individual (LDCT/ESI), and programmes’ effectiveness using quality-adjusted life years (QALY) gained from screening, lung cancer-specific mortality reduction (MR), and number of screen-detected lung cancer cases. We used validated natural history, smoking history generator, and risk prediction models to inform our analysis. Sensitivity analysis of key inputs was conducted. Findings ENGAGE was the best performing strategy. Among the structured policies, adaptive biennial-to-annual at age 65 was the best strategy requiring 24% less LDCT/DA and 60% less LDCT/ESI compared to TF2021, but yielded 105 more deaths per 100,000 screen-eligible individuals (10.2% vs. 11.8% MR for TF2021, p = 0.28). Fixed annual screening was the most effective strategy but the least efficient and was ranked as the fifth best strategy. All strategies yielded similar QALYs gained. Adherence levels did not affect the rankings. Interpretation Adaptive lung cancer screening strategies that start with biennial and switch to annual screening at a prespecified age perform well and warrant further consideration, especially in settings with limited availability of CT scanners and radiologists. Funding National Cancer Institute.
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    Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification
    (Elsevier, 2024) Hosseinian, Seyedmohammadhossein; Hemmati, Mehdi; Dede, Cem; Salzillo, Travis C.; van Dijk, Lisanne V.; Mohamed, Abdallah S. R.; Lai, Stephen Y.; Schaefer, Andrew J.; Fuller, Clifton D.
    Purpose Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible. Methods and Materials The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors. Results The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index. Conclusions This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.
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    MR-Guided Adaptive Radiotherapy for OAR Sparing in Head and Neck Cancers
    (MDPI, 2022) Mulder, Samuel L.; Heukelom, Jolien; McDonald, Brigid A.; Van Dijk, Lisanne; Wahid, Kareem A.; Sanders, Keith; Salzillo, Travis C.; Hemmati, Mehdi; Schaefer, Andrew; Fuller, Clifton D.
    MR-linac devices offer the potential for advancements in radiotherapy (RT) treatment of head and neck cancer (HNC) by using daily MR imaging performed at the time and setup of treatment delivery. This article aims to present a review of current adaptive RT (ART) methods on MR-Linac devices directed towards the sparing of organs at risk (OAR) and a view of future adaptive techniques seeking to improve the therapeutic ratio. This ratio expresses the relationship between the probability of tumor control and the probability of normal tissue damage and is thus an important conceptual metric of success in the sparing of OARs. Increasing spatial conformity of dose distributions to target volume and OARs is an initial step in achieving therapeutic improvements, followed by the use of imaging and clinical biomarkers to inform the clinical decision-making process in an ART paradigm. Pre-clinical and clinical findings support the incorporation of biomarkers into ART protocols and investment into further research to explore imaging biomarkers by taking advantage of the daily MR imaging workflow. A coherent understanding of this road map for RT in HNC is critical for directing future research efforts related to sparing OARs using image-guided radiotherapy (IGRT).
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    Optimized decision support for selection of transoral robotic surgery or (chemo)radiation therapy based on posttreatment swallowing toxicity
    (Wiley, 2023) Hemmati, Mehdi; Barbon, Carly; Mohamed, Abdallah S.R.; van Dijk, Lisanne V.; Moreno, Amy C.; Gross, Neil D.; Goepfert, Ryan P.; Lai, Stephen Y.; Hutcheson, Katherine A.; Schaefer, Andrew J.; Fuller, Clifton D.
    Background A primary goal in transoral robotic surgery (TORS) for oropharyngeal squamous cell cancer (OPSCC) survivors is to optimize swallowing function. However, the uncertainty in the outcomes of TORS including postoperative residual positive margin (PM) and extranodal extension (ENE), may necessitate adjuvant therapy, which may cause significant swallowing toxicity to survivors. Methods A secondary analysis was performed on a prospective registry data with low- to intermediate-risk human papillomavirus–related OPSCC possibly resectable by TORS. Decision trees were developed to model the uncertainties in TORS compared with definitive radiation therapy (RT) and chemoradiation therapy (CRT). Swallowing toxicities were measured by Dynamic Imaging Grade of Swallowing Toxicity (DIGEST), MD Anderson Dysphagia Inventory (MDADI), and the MD Anderson Symptom Inventory–Head and Neck (MDASI-HN) instruments. The likelihoods of PM/ENE were varied to determine the thresholds within which each therapy remains optimal. Results Compared with RT, TORS resulted in inferior swallowing function for moderate likelihoods of PM/ENE (>60% in short term for all instruments, >75% in long term for DIGEST and MDASI) leaving RT as the optimal treatment. Compared with CRT, TORS remained the optimal therapy based on MDADI and MDASI but showed inferior swallowing outcomes based on DIGEST for moderate-to-high likelihoods of PM/ENE (>75% for short-term and >40% for long-term outcomes). Conclusion In the absence of reliable estimation of postoperative PM/ENE concurrent with significant postoperative PM, the overall toxicity level in OPSCC patients undergoing TORS with adjuvant therapy may become more severe compared with patients receiving nonsurgical treatments thus advocating definitive (C)RT protocols.