## 2 Materials and Methods
### Patient Imaging and Manual Contouring
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Patient**} & \multirow{2}{*}{**Gender**} & \multirow{2}{*}{**Age**} & \multicolumn{2}{c}{**Tumor Area**} & \multirow{2}{*}{**Overall Stage**} & \multirow{2}{*}{**Cancer Type**} \\  & & & & **(cm2)** & & & \\ \hline \multirow{10}{*}{Liver} & 1 & F & 65 & 36.2 & III & Rectal adenocarcinoma \\  & 2 & M & 69 & 11.5 & I & HCC \\  & 3 & M & 70 & 24.2 & IV & Sigmoid colon adenocarcinoma \\  & 4 & M & 57 & 2.8 & I & HCC \\  & 5 & M & 64 & 2.0 & II & HCC \\  & 6 & M & 63 & 3.7 & IVB & Nasopharyngeal carcinoma \\  & 7 & M & 65 & 3.1 & IVA & Colorectal carcinoma \\  & 8 & M & 59 & 2.4 & IV & Adenocarcinoma \\  & 9 & M & 68 & 1.5 & IIB & Rectal adenocarcinoma \\  & 10 & F & 82 & 6.0 & IV & Colorectal cancer \\  & 11 & M & 71 & 6.2 & I & HCC \\ \hline \multirow{10}{*}{Prostate} & 12 & M & 66 & 23.3 & IIA & Prostatic adenocarcinoma \\  & 13 & M & 71 & 14.0 & IIIB & Prostatic adenocarcinoma \\  & 14 & M & 75 & 21.8 & IIB & Prostatic adenocarcinoma \\  & 15 & M & 62 & 12.2 & IIIB & Prostatic adenocarcinoma \\  & 16 & M & 66 & 15.9 & I & Prostatic adenocarcinoma \\  & 17 & M & 76 & 13.7 & I & Prostatic adenocarcinoma \\  & 18 & M & 77 & 24.5 & IIC & Prostatic adenocarcinoma \\  & 19 & M & 70 & 18.7 & IIB & Prostatic adenocarcinoma \\  & 20 & M & 69 & 8.4 & IIIC & Prostatic adenocarcinoma \\  & 21 & M & 63 & 4.7 & IIB & Prostatic adenocarcinoma \\  & 22 & M & 66 & 22.2 & IIC & Prostatic adenocarcinoma \\  & 23 & M & 70 & 20.1 & IIB & Prostatic adenocarcinoma \\ Prostate & 24 & M & 66 & 22.8 & IIC & Prostatic adenocarcinoma \\  & 25 & M & 77 & 26.9 & IIC & Prostatic adenocarcinoma \\  & 26 & M & 69 & 13.3 & IIIC & Prostatic adenocarcinoma \\  & 27 & M & 63 & 9.2 & IIB & Prostatic adenocarcinoma \\  & 28 & M & 62 & 11.6 & IIIB & Prostatic adenocarcinoma \\  & 29 & M & 69 & 13.9 & IIIC & Prostatic adenocarcinoma \\  & 30 & M & 66 & 19.7 & IIC & Prostatic adenocarcinoma \\  & 31 & M & 66 & 13.1 & I & Prostatic adenocarcinoma \\  & 32 & M & 75 & 26.4 & IIB & Prostatic adenocarcinoma \\  & 33 & M & 63 & 12.7 & IIB & Prostatic adenocarcinoma \\  & 34 & M & 70 & 14.3 & IIB & Prostatic adenocarcinoma \\  & 35 & M & 77 & 17.7 & IIC & Prostatic adenocarcinoma \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the liver, prostate (sagittal: P12–22, coronal: P23–27, axial: P28–35), and lung cancer patients included in this study. F: female; M: male; HCC: hepatocellular carcinoma; NSCLC: non-small-cell lung cancer; SCLC: small-cell lung cancer; SD: standard deviation. For each patient, an expert (radiation oncologist) manually contoured the gross tumor volume in 130 consecutive images using either Computational Environment for Radiotherapy Research (CERR) [43] or 3D Slicer (version 4.11) [44] software. In this study, 130 images/patient was chosen to balance the burden of manual contouring and obtaining a sufficient number of testing images. This dataset was used to train the algorithm, as well as to serve as a reference for comparison to autocontours by dividing into 30 training, 30 validation, and 70 testing images. Only the training images were used to learn the trainable parameters (i.e., neural network weights) of the U-Net-based algorithm. The validation images were used to (1) monitor the prediction accuracy of a given model built by the current set of hyperparameters and to (2) apply early stopping (details in Section 2.3) to prevent overfitting. The testing images were unseen by the algorithm during HPO and training and were only used to evaluate the accuracy of the final, hyperparameter (HP)-optimized network [35]. The 30-30-70 ratio was empirically chosen to include at least two respiratory cycles in the training images and to use the same number of validation images as training images. Regarding the data size, 47 patients were considered to be reasonable for this pilot study because our aim is to demonstrate patient-specific HPO and training of the algorithm to autocontour tumors that are unique to each patient.