Development of Clinically High Efficient Platforms for Individualised Treatment of Cervix Cancer

Condition(s) studied

Study summary

Retrospective study utilizing patient data to develop and validate Machine Learning application. Available imaging data sets of patients who have completed treatment will be used to develop Normal tissue complication probability and Tumour control probability

Hypothesis Integrating existing radiation treatment information, quantitative imaging and patient outcome data from completed and ongoing clinical trials will allow development of knowledge based systems for efficient treatment delivery and allow selection of patients for intensified treatment approaches in cervix cancer.

Eligibility

Primary outcome measure(s)

• Generation of software for automated target delineation for cervix cancer — 3 years
  1\. To develop and validate automated platforms for target delineation and planning for cervix cancer in time efficient manner through a. Machine learning based detection of abnormal cancerous tissues in multimodality medical diagnostic images. b . To train machine base systems for automated planning of external radiation and brachytherapy for gynaecological cancers.
• Development and validation of Normal Tissue Complication Plots — 3 years
  2\. To use existing databases and radiation dose maps, imaging texture features and adverse events data for machine learning to develop "normal tissue complication plots "and to identify cervix cancer patient subgroups that may benefit from advanced radiation techniques (like proton treatment)
• Identify "high risk patient population" that may benefit from intensification of treatment in future — 3 years
  3\. To use advanced image texture analysis within ongoing institutional and collaborative clinical trials to identify "high risk patient population" that may benefit from intensification of treatment in future

Trial sites (1)

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