Patent Application
20230377745
The invention relates to clinical decision support systems and more particularly to a method of establishing a clinical decision support system for SPC risk evaluation among patients with colorectal cancer using a prediction model and visualization so that a physician can evaluate risk of SPC among patients with colorectal cancer, make a correct clinical decision, and provide a patient appropriate advice.
A second primary cancer (SPC) is a second, unrelated cancer in a person who has previously experienced another cancer at any time. Both success rate of cancer treatment and survival rate increase due to effective cancer screening test and improved treatment. But the number of persons diagnosed with SPC also increases. SPC is the main cause of decreasing cancer survival rate. To the worse extent, SPC not only decreases the success rate of cancer treatment but also decreases quality of life of a patient with SPC. Thus, an early detection of SPC is critical to the disease-free survival in patients with cancer.
Currently, a patient can regularly take a cancer screening test with no item on SPC diagnosis. Therefore, risk of SPC of the patient cannot be evaluated. A patient may lose the chance of early finding of SPC. Furthermore, there is little clinical practice or technology on evaluating risk of SPC after colorectal cancer.
Therefore, it is necessary to provide a method of establishing a clinical decision support system for SPC risk evaluation among patients with colorectal cancer, in which a physician can use the method to evaluate risk of SPC after colorectal cancer, make a correct clinical decision, and give a patient appropriate advice.
It is therefore one object of the invention to provide a method of establishing a clinical decision support system for SPC risk evaluation among patients with colorectal cancer using a prediction model and visualization comprising combining a plurality of cancer characteristics into a cancer characteristic assembly of SPC risk evaluation; obtaining clinical data of a plurality of first participants corresponding to the cancer characteristic assembly of SPC risk evaluation to establish a database of SPC risk evaluation; entering the database of SPC risk evaluation into a machine learning algorithm; using the machine learning algorithms to establish a SPC risk evaluation model; using a characteristics interpreter to analyze the SPC risk evaluation model; calculating a risk value of each cancer characteristics; presenting the risk values in graphics to establish a clinical decision support system with visualization; obtaining clinical data of a plurality of second participants corresponding to the characteristic assembly of SPC risk evaluation; inputting the clinical data into the clinical decision support system; using the machine learning algorithms for comparison and analysis; predicting risk for SPC; calculating a risk value of each cancer characteristics with respect to each patient; presenting the risk values on the clinical decision support system using visualization; giving suggestions of decreasing the risk for SPC with respect to each cancer characteristics; and monitoring changes of the risk for SPC based on the presentation shown on the clinical decision support system.
Preferably, the value of each cancer characteristic is a Shapley value or a significance of a feature.
The risk of SPC among patients with colorectal cancer is increased when the Shapley value is positive, and the risk of SPC among patients with colorectal cancer is decreased when the Shapley value is negative.
Preferably, the presentation is a bar chart, pie chart, line chart or any combination thereof.
The invention has the following advantages and benefits in comparison with the conventional art:
The method uses the cancer characteristic assembly of SPC risk evaluation and the machine learning algorithms to establish the SPC risk evaluation model, and finally establish the clinical decision support system using visualization so that a medical employee can do an overall evaluation of a patient. The medical employee can take into account many characteristics of the patient because the characteristic assembly of SPC risk evaluation includes different cancer characteristics, thereby greatly increasing correctness and effectiveness of SPC risk evaluation. By presenting the clinical decision support system using visualization, a clinical physician can conveniently and quickly make a clinical decision in a simple manner. The clinical decision support system can show value changes of each cancer characteristics with respect to the risk of SPC in real time. Therefore, the physician can evaluate the risk for SPC based on increased risk value and decreased risk value with respect to each cancer characteristics prior to giving a patient appropriate advice.
The above and other objects, features, and advantages of the invention will become apparent from the following detailed description taken with the accompanying drawings.
Referring to
Referring to
Regarding admission and exclusion conditions of participants and number thereof, the participants are required to be colorectal cancer patients and no outside participants are recruited since the participants are required to be previous cancer patients of the medical center.
Retrospective period of the embodiment is from Jan. 1, 2004 to Dec. 31, 2018.
The method comprises:
Beneficial effects of the method of the invention are detailed below. Taking advantage of the characteristic interpreter 18, a physician can advise a person advice of predicted risk of SPC. For example, a person may have an increased risk of SPC because the primary cancer treatment is surgery. Fortunately, the risk of SPC is decreased because the person appropriately controls his or her BMI and does not smoke. Therefore, a physician can adjust system parameters (e.g., BMI) to monitor changes in risk for SPC. The physician can advise the person to reasonably decrease weight if the physician finds that BMI control can decrease the risk of SPC.
Interface simulations of the clinical decision support system 20 according to the first and second preferred embodiments with respect to different participants are shown in
As shown in
After the prediction button has been clicked, details of risk for SPC are shown in
Regarding each cancer characteristic 101 of the first participant, the primary site, which is the right colon, has a highest Shapley value of 0.138. It means that the primary site, the right-sided colon increases the risk of SPC of the first participant and has the greatest impact. Shapley values representing the risk for SPC of other cancer characteristics 101 (e.g., tumor size, smoking, alcohol consumption, betel nut chewing, age, gender, radiation therapy and surgical margins of the primary site) are gradually decreased. The physician can give advice to the first participant based on the risk values of the cancer characteristics 101 so as to help the first participant to decrease the risk of SPC. In addition, the physician can adjust parameters of one or more of the cancer characteristics 101 to monitor changes of the risk for SPC thereof.
As shown in
After the prediction button has been clicked, details of risk for SPC are shown in
Regarding each cancer characteristic 101 of the second participant, the cancer stage has a lowest Shapley value of −0.176. This means that the second stage of the cancer decreases the risk for SPC of the second participant and has the greatest impact. Shapley values that represent the risk of SPC of other cancer characteristics 101 (e.g., age, tumor size, primary site, and region lymph nodes positive) are gradually increased.
It is envisaged by the invention that a medical employee can take many cancer characteristics of a patient into consideration prior to evaluating the risk for SPC and making a correct clinical decision.
Preferably, the machine learning algorithm 14 uses logistic regression, multivariate adaptive regression splines (MARS), decision tree classifiers, rule-based classifier, nearest neighbor classifiers, naïve Bayes classifier, artificial neural network, deep learning, support vector machine (SVM), random forest, eXtreme Gradient Boosting (XGBoost), categorical boosting, light gradient boosting machine (light GBM), ensemble learning methods, bagging and boosting-based classifiers, adaptive boosting-based classifiers, fuzzy set-based classifiers, genetic algorithms-based (GA-based) classifiers, genetic programming-based (GP-based) classifiers, meta heuristic-based classifiers, linear and nonlinear discriminant analysis, or any combination thereof.
Preferably, the cancer characteristic interpreter 18 includes local interpretable model-agnostic explanations (LIME), deep learning important features (DeepLIFT), layer-wise relevance propagation (LRP), Classic Shapley Value Estimation, Shapley Additive Explanation (SNAP), Shapley value-based model explanations, or any combination thereof.
Preferably, the cancer characteristic assembly 10 of SPC risk evaluation includes sex, birth year, initial data of first diagnosis, initial date of pathology diagnosis, method of confirming cancer, primary site, handedness, tissue type, sexual orientation code, grade and differentiation, clinical tumor size, pathology tumor size, number of checked region lymph nodes, positive region lymph nodes, distances of surgical margins and tumor cells, surgical margins of the primary site, cancer stage version, clinical cancer stage T, clinical cancer stage N, clinical cancer stage M, clinical cancer stage, pathology cancer stage T, pathology cancer stage N, pathology cancer stage M, pathology cancer stage, surgical therapy for tumor primary site by hospital, method of surgery for tumor primary site by hospital, surgical range of region lymph nodes by hospital, date of initial surgery, radiation therapy at the primary site by hospital, method of radiation therapy at the primary site, radiation dose for external body part radiation therapy at primary site, number of external body part radiation therapy, date of first external body part radiation therapy by hospital, date of final external body part radiation therapy by hospital, proximity radiation therapy by hospital, dose of proximity radiation therapy, chemotherapy performed by hospital, synchronous chemotherapy and radiation therapy, method of chemotherapy, times of performed chemotherapy, initial date of chemotherapy performed by hospital, hormone therapy by hospital, initial date of hormone therapy by hospital, date of final correspondence or death date, existence status, cancer status, date of first reoccurrence of SPC, type of first reoccurrence of SPC, causes of death, carcinoembryonic antigen (CEA) value, tumor decrease grade, pathology annular removal margins, nerve incursion, Kirsten rat sarcoma virus (KRAS) value, finding of intestine blockage or not before or after surgery, finding of intestine perforation or not before or after surgery, or any combination thereof.
Although the invention has been described in terms of preferred embodiments, those skilled in the art will recognize that the invention can be practiced with modifications within the spirit and scope of the appended claims.
