Israel Medical Association Journal

Background: For locally advanced rectal cancer patients a watch-and-wait strategy is an acceptable treatment option in cases of complete tumor response. Clinicians need robust methods of patient selection after neoadjuvant chemoradiation.

Objectives: To predict pathologic complete response (pCR) using computer vision. To analyze radiomic wavelet transform to predict pCR.

Methods: Neoadjuvant chemoradiation for patients with locally advanced rectal adenocarcinoma who passed computed tomography (CT)-based simulation procedures were examined. Gross tumor volume was examind on the set of CT simulation images. The volume has been analyzed using radiomics software package with wavelets feature extraction module. Statistical analysis using descriptive statistics and logistic regression was performed was used. For prediction evaluation a multilayer perceptron algorithm and Random Forest model were used.

Results: In the study 140 patients with II–III stage cancer were included. After a long course of chemoradiation and further surgery the pathology examination showed pCR in 38 (27.1%) of the patients. CT-simulation images of tumor volume were extracted with 850 parameters (119,000 total features). Logistic regression showed high value of wavelet contribution to model. A multilayer perceptron model showed high predictive importance of wavelet. We applied random forest analysis for classifying the texture and predominant features of wavelet parameters. Importance was assigned to wavelets.

Conclusions: We evaluated the feasibility of using non-diagnostic CT images as a data source for texture analysis combined with wavelets feature analysis for predicting pCR in locally advanced rectal cancer patients. The model performance showed the importance of including wavelets features in radiomics analysis.

Background: With the recent introduction of high-sensitivity troponin (hsTn), the incremental benefit of stress myocardial perfusion imaging (MPI) in the evaluation of patients who present to the emergency department (ED) with acute coronary syndrome (ACS) is unclear.

Objectives: To assess the added value of stress MPI in low-risk ACS patients with normal range hsTnI.

Methods: We analyzed all patients who were hospitalized at our medical center from February 2016 to November 2017, who presented with low-risk ACS and underwent stress MPI, and in whom hsTnI was in the normal range after the introduction of hsTnI.

Results: During the study period, 161 patients were admitted with a diagnosis of unstable angina (i.e., ACS with normal range hsTnI) and underwent stress MPI during index admission. The study population included 52/161 patients (31.7%) with low-risk ACS who had no indication for initial invasive strategy. No patients had positive MPI. One patient underwent coronary angiography due to suggestive history; however, he did not have a significant coronary artery disease and had no indication for percutaneous coronary intervention.

Conclusions: In patients with low-risk ACS and normal range hsTnI without additional high-risk features, stress MPI has little additional value for the correct diagnosis and management. Prospective studies are warranted to confirm whether resting hsTnI could serve as a powerful triage tool in chest pain patients in the ED before diagnostic testing and thus, improve patient management.