Browsing by Subject "NSCLC"

Purpose: To compare radiomics and deep-learning (DL) methods for predicting NSCLC surgical treatment failure. Methods: A cohort of 83 patients undergoing lobectomy or wedge resection for early-stage NSCLC from our institution was studied. There were 7 local failures and 16 non-local failures (regional and/or distant). Gross tumor volumes (GTV) were contoured on pre-surgery CT datasets after 1mm3 isotropic resolution resampling. For the radiomics analysis, 92 radiomics features were extracted from the GTV and z-score normalizations were performed. The multivariate association between the extracted features and clinical endpoints were investigated using a random forest model following 70%-30% training-test split. For the DL analysis, both 2D and 3D model designs were executed using two different deep neural networks as transfer learning problems: in 2D-based design, 8x8cm2 axial fields-of-view(FOVs) centered within the GTV were adopted for VGG-16 training; in 3D-based design, 8x8x8 cm3 FOVs centered within the GTV were adopted for U-Net’s encoder path training. In both designs, data augmentation (rotation, translation, flip, noise) was included to overcome potential training convergence problems due to the imbalanced dataset, and the same 70%-30% training-test split was used. The performances of the 3 models (Radiomics, 2D-DL, 3D-DL) were tested to predict outcomes including local failure, non-local failure, and disease-free survival. Sensitivity/specificity/accuracy/ROC results were obtained from their 20 trained versions. Results: The radiomics models showed limited performances in all three outcome prediction tasks. The 2D-DL design showed significant improvement compared to the radiomics results in predicting local failure (ROC AUC = 0.546±0.056). The 3D-DL design achieved the best performance for all three outcomes (local failure ROC AUC = 0.768 ± 0.051, non-local failure ROC AUC = 0.683±0.027, disease-free ROC AUC = 0.694±0.042) with statistically significant improvements from radiomics/2D-DL results. Conclusions: 3D-DL execution outperformed the 2D-DL in predicting clinical outcomes after surgery for early-stage NSCLC. By contrast, classic radiomics approach did not achieve satisfactory results.

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.