An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

OBJECTIVES

Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors.

METHODS

We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards.

RESULTS

In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set.

CONCLUSION

Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.

The association of dementia on perioperative complications following primary total hip arthroplasty for femoral neck fractures

INTRODUCTION

Studies evaluating the association of dementia in patients undergoing total hip arthroplasty (THA) for femoral neck fractures are limited. The aim was to investigate whether patients who have dementia undergoing THA for femoral neck fractures have higher rates of (1) in-hospital lengths of stay (LOS); 2) complications (medical and prostheses-related); and 3) healthcare expenditures.

METHODS

A retrospective query using the PearlDiver database from January 1st, 2005 to March 31st, 2014 to identify patients with dementia undergoing primary total hip arthroplasty for the treatment of femoral neck fractures was performed. Dementia patients were 1:5 ratio matched to controls which yielded 22,758 patients in the study with (n = 3,798) and without (n = 18,960) dementia. Primary outcomes included comparing LOS, complications, and costs. A logistic regression was constructed to calculate the odds-ratios (OR) of dementia on complications. A p-value less than 0.004 was significant.

RESULTS

Dementia patients had longer LOS (7-days vs. 6-days, p < 0.0001) and higher incidence and odds of medical complications (41.52 vs. 17.77%; OR 3.76, p < 0.0001), including cerebrovascular events (5.66 vs. 1.64%; OR 2.35, p < 0.0001), pneumoniae (9.98 vs. 3.82%; OR 1.82, p < 0.0001), and acute kidney injury (8.37 vs. 3.27%; OR 1.62, p < 0.0001). Study group patients had higher frequency of prostheses-related complications (9.53 vs. 8.31%; OR: 1.16, p = 0.012). The study group had greater total healthcare expenditures ($28,879.57 vs. $26,234.10, p < 0.0001) when analyzing ninety-day episode of care charges.

CONCLUSION

Patients with dementia undergoing THA due to femoral neck fracture have increased LOS, medical and prostheses-related complications, and cost of care compared to their counterparts.