Pre-treatment Radiomics Models for Clinical Outcomes in Early-stage Non-Small Cell Lung Cancer (NSCLC)

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2021

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Abstract

Lung cancer in accounts for 13% of all new cancer diagnoses and is the leading cause of cancer mortality in the United States (Bogart, 2017, Howlader, 2020). Non-small cell lung cancer (NSCLC) in particular, accounts for 80-85% of lung cancer diagnoses and is estimated to cause more than 130,000 deaths this year (American Cancer Society, 2021). Currently, the standard of care for early-stage NSCLC is surgery, with stereotactic body radiation therapy (SBRT) is becoming more accepted as the primary treatment option for patients who are medically inoperable. It remains controversial as to which method is optimal for marginal surgical patients, but it has been shown that SBRT and sublobar resection provide similar local tumor control rates and clinical outcomes in stage I NSCLC (Ackerson, 2018).The goal of this research work was to develop pre-treatment radiomic models for surgical NSCLC patients to predict cancer recurrence. This was done by investigating two specific aims. The first was to (1.) build radiomic models based on pre-treatment CT images from surgical patients and evaluate their performance in predicting cancer recurrence and the second was to (2.) build radiomic models based on pre-treatment CT images from surgical patients and evaluate their performance in predicting cancer recurrence. Radiomic features were extracted from the contoured GTV’s from pre-treatment CT scans of surgical and SBRT patients. To investigate the first aim, multivariate models were trained and tested on only surgical patients to find associations between the extracted features and each clinical outcome. To investigate the second aim, these models were first trained on surgical data and tested on SBRT data to investigate the generalizability of each model across treatment modalities. Next, models were trained and tested on a pooled dataset to investigate potential associations of radiomic features with cancer recurrence independent of treatment. Models were evaluated by creating ROC curves and calculating the area under these curves (AUC’s). Models trained and tested on surgical patients showed a stronger association between radiomic features and non-local failure (maximum AUC of 0.82 ± 0.04) and a poor association with local failure (maximum AUC of 0.57 ± 0.04). This may suggest that radiomic features have limited value in predicting local recurrence since the GTV which is used in calculating these features is no longer in the body post-treatment. Despite this, it is difficult to draw strong conclusions based on the variability in the image parameters of surgical patients, such as slice thickness and x-ray tube current, which have been shown to affect feature values (Midya, 2018, Kim, 2019). This is supported by the degraded performance in these models when SBRT data was introduced, further increasing image variability.

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Shaffer, Nathan (2021). Pre-treatment Radiomics Models for Clinical Outcomes in Early-stage Non-Small Cell Lung Cancer (NSCLC). Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/23384.

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