Research That Redefines
Patient Outcomes

Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients are overdosed and experience chemotherapy toxicities (DLT). We evaluated the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting toxicities in colon cancer patients.

Our group showed that compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict chemotherapy toxicities in Colorectal Cancer patients receiving chemotherapy.

In recent years, certain body composition measures, assessed by computed tomography (CT), have been found to be associated with chemotherapy toxicities. This review aimed to explore available data on the relationship between skeletal muscle and adiposity, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular and intermuscular adipose tissue and their association with chemotherapy toxicity in colorectal cancer patients.

Low skeletal muscle was found to be associated with chemotherapy toxicities in Colorectal Cancer patients. We also found that more studies are needed to confirm these associations with an emphasis on a more coherent body composition definition and an approach to its assessment, especially regarding sarcopenia.

Hartmann's reversal, a complex elective surgery, demands careful planning and patient optimisation to help reduce postoperative complications. Preoperative evaluation of body composition has been useful in identifying patients at high risk of short-term postoperative outcomes following colorectal cancer surgery. Our group sought to explore the use of our in-house derived Artificial Intelligence (AI) algorithm to measure body composition within patients undergoing Hartmann's reversal procedure in the prediction of short-term postoperative complications.

We found that sarcopenia and sarcopenic obesity were related to postoperative complications following Hartmann's reversal. We concluded that body composition measured by a validated AI algorithm may be a beneficial tool for predicting short-term surgical outcomes for these patients.

This study utilised our AI algorithm on colon cancer patients to predict chemotherapy overdosing leading to toxicities seen in these patients. It also demonstrated that multiple lumbar levels (L1-L5) of body composition were superior in the prediction of chemotherapy overdosing over a single CT vertebral level.

There is mounting evidence that suggests sarcopenia can be used to predict survival outcomes in patients with colon cancer. However, the effect on locally advanced rectal cancer (LARC) is less clear. Our group sought to determine the association between sarcopenia on Overall Survival and Recurrence-free Survival (OS and RFS) in patients with LARC undergoing multimodal treatment.

We found that sarcopenia was found to be an independent risk factor for worse overall survival, but not recurrence free survival, in patients with locally advanced rectal cancer undergoing neo-adjuvant chemo-radiotherapy and surgery with curative intent.

Body composition analysis in colorectal cancer typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 lumbar level. The use of artificial intelligence allows for analysis of the entire L3 vertebra. The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures.

Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Our findings support the theory that only using a single CT slice will impact the estimate of body composition measurements.

This study demonstrated that compared to the current gold standard, this fully automated algorithm exhibited a high accuracy for assessing body composition and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in Colorectal cancer patients.

This study has utilized AI technology to measure body composition from the entire lumbosacral (L1-S5) region using multiple CT slices per patient and assessed the associations between body composition and clinical outcomes in rectal cancer patients who have undergone neoadjuvant therapy followed by surgery.

This study utilised our AI algorithm to accurately measure CT derived 3D body composition which outperformed traditional Body Surface Area (BSA) in predicting adverse events and toxicities in female patients with rectal cancer undergoing chemotherapy.