At PredicTx, research isn’t just an outcome, it’s the engine behind everything we build. Our team collaborates with leading clinicians, scientists, and institutions to push the boundaries of oncology care.
From chemotherapy dosing models to 3D imaging and composition analysis, our findings are shared through peer-reviewed publications and conference papers to advance both science and patient care.
Explore our latest publications below and see how PredicTx is contributing to the global development of cancer care.
14 November, 2025
This review highlights inconsistencies in body composition's role in predicting chemotherapy toxicities and outcomes in pancreatic cancer. Variability in study methodologies complicated comparisons. Standardising body composition analysis with attention to ethnic variations and treatment types is essential for refining personalised treatment strategies in pancreatic cancer.
16 May, 2025
Chemotherapy administration is a balancing act between giving enough to achieve the desired tumour response while limiting adverse effects. Chemotherapy dosing is based on body surface area (BSA). Emerging evidence suggests body composition plays a crucial role in the pharmacokinetic and pharmacodynamic profile of cytotoxic agents and could inform optimal dosing. This study aims to assess how lumbosacral body composition influences adverse events in patients receiving neoadjuvant chemotherapy for rectal cancer.
22 December, 2024
This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.
21 December, 2024
Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This study aimed to evaluate and compare the effectiveness of using three-dimensional (3D) CT body composition measures from the entire lumbar spine levels (L1–L5) versus a single vertebral level (L3), the current gold standard, in predicting chemotherapy DLT in colon cancer patients.
27 November, 2024
Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in patients using data from an entire three-dimensional (3D) region of the body. This study has utilized AI technology to measure BC from the entire lumbosacral (L1-S5) region and assessed the associations between BC and clinical outcomes in rectal cancer patients who have undergone neoadjuvant therapy followed by surgery.
15 April, 2024
Hartmann's reversal, a complex elective surgery, reverses and closes the colostomy in individuals who previously underwent a Hartmann's procedure due to colonic pathology like cancer or diverticulitis. It 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. We 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.
07 December, 2023
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.
17 October, 2023
Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures. Methods. A total of 2203 axial CT slices of the entire L3 level (4–46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately. Results. Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007). Conclusion. Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.
04 August, 2023
Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate 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 DLT in colon cancer patients.
20 May, 2023
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. We sought to determine the association between sarcopenia on Overall Survival and Recurrence-free Survival (OS and RFS) in patients with LARC undergoing multimodal treatment.