ANZCTR search results

In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by surgery is the current standard of care and ample evidence has accumulated supporting the view that complete pathological response (pCR) is a positive prognostic marker for improved outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could permit modification of treatment protocols for those patients unlikely to achieve pCR. Radiomics is a new entrant in the field of imaging where specific features are derived from the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The features thus extracted can then be used for prediction modelling similar to other -omics datasets. Preliminary investigations examining its utility have been performed and its applications have thus far focused on screening and survival prediction after treatment. Due to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence (AI) methods are ideally suited for analysing and modelling radiomic features. Machine Learning (ML) and Deep Learning (DL)\[utilising Convolutional Neural Networks (CNN)\] are both part of the AI framework. In contrast to ML, DL is a new entrant and has been utilised by some medical researchers for modelling using prediction-type algorithms. Besides significantly reducing the workflow associated with Radiomics-based research, feature engineering and modelling using DL are immune to the effects of incorrect ROI delineation. However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of a CNN is not known. This has been mitigated in part by the visualisation of activation maps directly on the image dataset to prove biological plausibility of predictions. The comparative performance of both types of modelling is also not known. Our objective is to investigate pCR probability in our study population using radiomics-based ML and AI-based modelling. We will also investigate the comparative performance of both modelling techniques. For DL based prediction modelling, we will attempt to provide biological plausibility on the basis of activation maps.

The purpose of this study is to assess the efficacy of the amivantamab and lazertinib combination, compared with osimertinib, in participants with epidermal growth factor receptor (EGFR) mutation (Exon 19 deletions \[Exon 19del\] or Exon 21 L858R substitution) positive, locally advanced or metastatic non-small cell lung cancer (NSCLC).

A two (2) part study to evaluate the safety, tolerability and PK of ADX-914

This is a phase 1b/2 study of KRT-232 combined with ruxolitinib in subjects with MF who have a suboptimal response after at least 18 weeks of treatment with ruxolitinib. The primary objective of the study is to determine a recommended phase 2 dose (RP2D) of KRT 232 in combination with ruxolitinib.

This study will evaluate the efficacy and safety of belantamab mafodotin in combination with pomalidomide and dexamethasone (Arm A) compared with that of combination of pomalidomide, bortezomib and dexamethasone (Arm B) in participants with relapsed/refractory multiple myeloma (RRMM).

An International Multi-Centre Randomised Adaptive Platform Clinical Trial to Assess the Clinical, Virological and Immunological Outcomes in Patients with SARS-CoV-2 Infection (COVID-19).

This study will evaluate a physical activity intervention for children during acute cancer treatment. The intervention aims to encourage children to participate in increased levels of physical activity and reduce the amount of time they spend engaged in sedentary activities. This study also aims to evaluate different ways of assessing physical function in order to determine the best outcome measure to use for children during acute cancer treatment.

This is an open-label, multi-center, multi-cohort, Phase 2 study to evaluate the efficacy and safety of trastuzumab deruxtecan (T-DXd) for the treatment of selected HER2-expressing tumors. This study will consist of Part 1 which includes 7 cohorts of: urothelial bladder cancer, biliary tract cancer, cervical cancer, endometrial cancer, ovarian cancer, pancreatic cancer, and rare tumors; and Part 2 which includes 5 cohorts A to E of: A) any tumor type that is HER2 IHC 3+ (excluding breast, gastric cancer, and colorectal cancer), B) any tumor type that is HER2 IHC 2+/ISH+ (excluding breast, gastric cancer, and colorectal cancer), C) HER2 IHC 2+ or 1+ endometrial cancer, D) HER2 IHC 2+ or 1+ ovarian cancer, and E) HER2 IHC 2+ or 1+ cervical cancer. Study hypothesis: Trastuzumab deruxtecan will show meaningful clinical activity and a favorable risk benefit profile in selected HER2-expressing solid tumors.

A Phase I, safety and tolerability study of Vitargus® in vitrectomy surgery

This is a phase I/II, multi-center, open-label study of YH003 in combination with Toripalimab (anti-PD-1 mAb). The study is comprised of a dose escalation part (Part I) exploring escalating doses of YH003 in combination with fixed dose toripalimab in subjects with advanced solid tumors (Part I), followed by an expansion part (Part II) with three expansion cohorts.