Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study

Abstract

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

Development and validation of a machine learning-based predictive model to improve the prediction of inguinal status of anal cancer patients: A preliminary report

“…Modern highly intensity modulated radiation techniques (IMRT, Volumetric-Arc and Rotational Radiation Therapy) allow an optimal coverage of the target volumes and a better sparing of the surrounding normal tissues, with a reduction of the toxicity. In this scenario , the potential interest of a method allowing the further reduction of the treatment fields (and then of the toxicity) could be easily argued [ 16 ].…”

Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study

“…A hyperplane is defined as the set of all points x ∈ R dimension that satisfy h ( x )=0, where h ( x ) is the function of the hyperplane, as follows in equation (1) in d dimensions: 7 , 8 …”

“…In the case of the nonlinear model, the kernel method is used to distinguish the linear machine. 9 A hyperplane is defined as the set of all points x ∈ R dimension that satisfy h(x) = 0, where h(x) is the function of the hyperplane, as follows in equation (1) in d dimensions: 7,8 h x ð Þ = w T x + b (1) In this study, we modelled the SVM algorithm using dose-volume input in test and training models as shown in the flow chart in Figure 2. Figure 2a shows the entire analysis system from the treatment planning data, including quantitative analysis for homogeneity index, conformity index and conformation number and dosimetrical indices, TCP, NTCP of biological indices in addition to a big data-based prediction algorithm, to the results.…”

“…4,5 In addition, big data analysis studies of prostate cancer using machine learning approaches have been performed. [6][7][8] According to Çınar et al, prostate cancer is currently the most common type of cancer in men except lung cancer. 6 Therefore, we used prostate cancer cases as the model system.…”