System and method for generation and use of radiation outcome prediction score in patients undergoing radiotherapy

A system comprising:

• a processor storing instructions in non-transitory memory that, when executed, cause the processor to:
• receive raw data from multiple sources for a patient undergoing a treatment comprising radiation therapy, wherein the multiple sources comprise one or more of electronic medical records, imaging data, genomics data, metabolomics data, anatomical data, physiological data, and data from treatment planning systems;
• convert and combine the raw data into standardized usable record structure comprising input parameters for further processing, wherein the input parameters comprise one or more of tumor specific variables of a tumor, patient specific parameters, gene specific parameters, physics variables, metabolic variables, radiation variables, and treatment planning specific parameters of the patient during therapy and post therapy;
• store standardized data into records;
• perform analytics to gain sights using one or more machine learning models trained by utilizing plurality of patient records;
• provide knowledge represented through the machine learning models to aid a physician suggesting specification of treatment parameters for the patient, wherein the
• treatment parameters comprise one or more of total radiation dose, dose per fraction, overall treatment time, fractionation, beam type, beam energy, physical arrangements, and planned doses to critical normal tissue, wherein the machine learning models comprise deep learning and artificial neural networks;
• present an overall cumulative risk score predicting a treatment response and side effects of the radiation therapy, wherein the overall cumulative risk score comprises compromising one or more of a disease recurrence score, a time to recurrence score, a recovery probability score, and an adverse reaction score for the treatment based on the input parameters, wherein the overall cumulative risk score is a personalized score summarizing survival, adverse events, and the treatment response of the patient;
• provide a probabilistic predicting system adapted to be interactive to explore an impact of adjusting the treatment parameters on the overall cumulative risk score configured to adjust the treatment parameters;
• obtain new treatment parameters via the probabilistic predicting system by the physician based on a real-time condition of the patient to optimize the treatment response, wherein the treatment response comprises a radiation dose to the tumor versus dose to normal tissue for tumor regression with the side effects being minimal to normal tissues;
• update the overall cumulative risk score by incorporating the new treatment parameters and the treatment response through a feedback system;
• specify the new treatment radiotherapy dosage parameters in a treatment plan for the patient;
• wherein the new treatment parameters are determined based on characteristics of the patient as per the knowledge gained by the machine learning models from past treatments data of the patient and wherein the new treatment parameters are
• obtained by one or more of increasing the total radiation dose, decreasing the total radiation dose, adjusting the fractionation, adjusting the dose per fraction, altering the overall treatment time, altering the beam type, adjusting the beam energy adjusting the physical arrangements, adjusting dose to the normal tissues, and incorporating additional therapies to managing the side effects and improve treatment outcomes;
• and
• wherein the system generates real-time, dynamic, comprehensive therapy outcome prediction score for the radiation therapy via the overall cumulative risk score for the patient.