###### Abstract  
This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (\(p<0.01\)). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors.  
Keywords:consolidation tumor ratio; deep learning; part-solid tumors; independent test; non-small cell lung cancer (NSCLC) +
Footnote †: journal: Medical  
suggested the feasibility of the CTR to predict patients' prognoses after stereotactic body radiation therapy (SBRT) [11; 12].  
To estimate the CTR in general, clinical physicians need to manually delineate both whole tumor regions and solid components in part-solid tumors [13]. Due to the different levels of experience and skill among physicians, manual contouring may lead to inter-observer and intra-observer variability in contours [14], resulting in uncertainties with random and/or systematic errors in estimating CTRs. To reduce these types of uncertainties, automated prediction approaches for CTRs for lung cancer patients treated with surgical resection have been developed based on deep learning (DL) [15; 16; 17]. [PERSON] et al. [15] introduced a DL-based CTR measuring approach for the prediction of tumor invasiveness for lung adenocarcinoma. [PERSON] et al. [16] assessed the invasiveness of lung cancer using a DL-based method for the prediction of 2D and 3D CTRs. [PERSON] et al. [17] evaluated the performance of a DL algorithm for CTR measurements in predicting the prognosis of lung adenocarcinoma. However, there have been no studies on automated prediction approaches of CTRs on treatment planning CT images for patients who underwent radiotherapy. In addition, the three studies did not assess the segmentation accuracy of their DL models with evaluation indices [e.g., Dice's similarity coefficient (DSC)].  
For the prediction of accurate CTRs, automated segmentation techniques for part-solid tumors of lung cancer are necessary. [PERSON] et al. [18; 19] investigated a deep learning segmentation (DLS) approach using a dense V-Net for SBRT cases with the three types of tumors [solid, part-solid, and ground-glass opacity tumors (GGO)] on treatment planning CT images. They reported a mean DSC of 0.822 for part-solid tumors [18]. The dense V-Net has shown the advantage for segmentation of small tumors including part-solid tumors [18; 19]. Therefore, the dense V-Net could be feasible for the segmentation of whole tumors and measurement of CTRs.  
The authors hypothesized that DLS approaches based on the dense V-Net can provide more accurate contours of whole tumors to measure CTRs on treatment planning CT images. In this study, an automated prediction approach of CTRs was proposed for part-solid tumors of patients treated with radiotherapy on treatment planning CT images using a DLS model based on the dense V-Net.