Towards a fully automatic workflow for investigating the dynamics of lung cancer cachexia during radiotherapy using cone beam computed tomography.

Objective.Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles.Approach.Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated.Main results.Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice similarity coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of -0.84 for the pCT dataset and -0.89 for the sCT dataset.Significance.This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome.

NMR-based metabolomics in real-time monitoring of treatment induced toxicity and cachexia in head and neck cancer: a method for early detection of high risk patients.

INTRODUCTION: Nutritional treatment in head and neck squamous cell carcinoma cancer (HNSCC) patients undergoing radio-/chemo-radiotherapy (RT/CHRT) is complex and requires a multidisciplinary approach. In this study the real-time dynamic changes in serum metabolome during RT/CHRT in HNSCC patients were monitored using NMR-based metabolomics. OBJECTIVES: The main goal was to find the metabolic markers that could help prevent of acute radiation sequelae (ARS) escalation. METHODS: 170 HNSCC patients were treated radically with RT/CHRT. Blood samples were collected weekly, starting from the day before the treatment and stopping within the week after the RT/CHRT completion, resulting in a total number of 1328 samples. 1H NMR spectra were acquired on Bruker 400 MHz spectrometer at 310 K and analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Additional statistical analyses were performed on the quantified metabolites. RESULTS: PCA has detected a group of distinct outliers corresponding to ketone bodies (3HB, Ace, AceAce). These outliers were found to identify the individuals at high risk of weight loss, mainly by the 3HB changes, which was confirmed by the patients' medical data. In the OPLS-DA models a transition from the lowest to the highest weight loss is seen, defining the metabolic time trajectories for the patients from the studied groups during RT/CHRT. 3HB is a relatively sensitive marker that allows earlier identification of the patients at higher risk of > 10% weight loss. CONCLUSION: Our findings indicate that metabolic alterations, characteristic for malnutrition or cachexia, can be detected already at the beginning of the treatment, making it possible to monitor the patients with a higher risk of weight loss.