Mr. Mohammadhadi Khorrami Profile

Mohammadhadi Khorrami

at Case Western Reserve Univ

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Publications (3)

Proceedings Article | 13 March 2019 Presentation + Paper

Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival

Mehdi Alilou, Pranjal Vaidya, Mohammadhadi Khorrami, Alexia Zagouras, Pradnya Patil, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi

Proceedings Volume 10950, 109501F (2019) https://doi.org/10.1117/12.2513648

KEYWORDS: Computed tomography, Tumors, Image segmentation, Lung cancer, Cancer, Lung, Feature extraction, Medical research

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Proceedings Article | 13 March 2019 Presentation + Paper

A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study

Mohammadhadi Khorrami, Mehdi Alilou, Prateek Prasanna, Pradnya Patil, Pirya Velu, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi

Proceedings Volume 10950, 109500R (2019) https://doi.org/10.1117/12.2513001

KEYWORDS: Tumors, Computed tomography, Lung cancer, Feature extraction, Cancer, Lung, Tissues, Medical research, Feature selection, Proteins

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Immune checkpoint inhibitors targeting the programmed cell death (PD)1/ L1 axis have been approved for treatment of chemotherapy refractory advanced non-small cell lung cancer (NSCLC) for a few years. While higher PD-L1 expression is associated with better outcomes after monotherapy with immune checkpoint inhibitors, it is not a perfect predictive biomarker for clinical benefit from immunotherapy, because some patients with low PD-L1 expression have sustained responses. In clinical practice, using radiological tools like Response Evaluation Criteria in Solid Tumors (RECIST), tends to underestimate the benefit of therapy. For instance, some patients treated with immunotherapy suffer from pseudoprogression while actually having a favorable response, RECIST in this setting is inadequate to capture the response. In this study we sought to explore whether radiomic texture features extracted from both inside and outside of the tumor from baseline CT scans were associated with overall patient survival (OS) in 139 NSCLC patients being treated with IO from two separate sites. Patients were divided into a discovery (D₁ = 50; nivolumab from Cleveland Clinic) and two validation sets (D₂ = 62 from Cleveland Clinic, D₃ = 27 from University of Pennsylvania Health System. Patients in the validation sets had been treated with different types of checkpoint inhibitor drugs including nivolumab, pembrolizumab, and atezolizumab. 454 radiomic texture features from within (intra-tumoral) and outside the tumor (peri-tumoral) were extracted from baseline contrast CT images. Following feature selection on the discovery set, a radiomic risk-score signature was generated by using least absolute shrinkage and selection operator. Using a Cox regression model, the association of the radiomic signature with overall survival (OS) was evaluated in the discovery and two validation sets. In addition, 95% confidence intervals (CI) and relative hazard ratios (HR) were calculated. Our results revealed that the radiomics signature was significantly associated with OS, both in the discovery set (HR = 5.06, 95%CI = 3, 8.55; p-value < 0.0001) and the two validation data sets (D₂: HR = 5.88, 95% CI = 2.19, 21.63, p-value = 0.0009; D₃: HR = 5.37, 95% CI = 1.74, 16.57, p-value = 0.0034). Our initial results appear to suggest that our radiomic signature could serve as a non-invasive way of predicting and monitoring response to checkpoint inhibitors for patients with non-small cell lung cancer.

SPIE Journal Paper | 18 April 2018 Open Access

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Mahdi Orooji, Mehdi Alilou, Sagar Rakshit, Niha Beig, Mohammadhadi Khorrami, Prabhakar Rajiah, Rajat Thawani, Jennifer Ginsberg, Christopher Donatelli, Michael Yang, Frank Jacono, Robert Gilkeson, Vamsidhar Velcheti, Philip Linden, Anant Madabhushi

JMI, Vol. 5, Issue 02, 024501, (April 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.024501

KEYWORDS: Computed tomography, Feature extraction, Data analysis, Chest, Feature selection, Lung cancer, Receivers, Tumors, Digital filtering, Image filtering

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