Visual Evoked Potential Can Predict Deterioration of Visual Function After Direct Clipping of Paraclinoid Aneurysm With Anterior Clinoidectomy.

BACKGROUND: The role of visual evoked potential (VEP) in direct clipping of the paraclinoid internal carotid artery (ICA) aneurysm remains uncertain. OBJECTIVE: To examine whether intraoperative neuromonitoring with VEP can predict deterioration of visual function after direct clipping of the paraclinoid ICA aneurysm with anterior clinoidectomy. METHODS: Among consecutive 274 patients with unruptured cerebral aneurysm, we enrolled 25 patients with paraclinoid ICA aneurysm treated by direct clipping after anterior clinoidectomy with intraoperative neuromonitoring with VEP in this study. We evaluated the visual acuity loss (VAL) and visual field loss (VFL) before surgery, 1 month after surgery, and at the final follow-up. RESULTS: The VAL at 1 month after surgery (VAL1M) and VAL at the final follow-up (Final VAL) were significantly related to the reduction rate of VEP amplitude at the end of surgery (RedEnd%), more than 76.5%, and the maximal reduction rate of VEP amplitude during surgery (MaxRed%), more than 66.7% to 70%. The VFL at 1 month after surgery (VFL1M) and the VFL at the final follow-up (Final VFL) were significantly related to MaxRed% more than 60.7%. CONCLUSION: VAL1M, Final VAL, VFL1M, and Final VFL could be significantly predicted by the value of RedEnd% and MaxRed% in direct clipping of Al-Rodhan group Ia, Ib, and II paraclinoid ICA aneurysms with anterior clinoidectomy.

Factors affecting global neurocognitive status and frontal executive functions in the early stage after surgical clipping of unruptured anterior circulation aneurysms with respect to keyhole clipping and conventional clipping.

PURPOSE: This study investigated the most significant factor for the preservation of the global neurocognitive status and frontal executive functions in the surgical clipping of unruptured anterior circulation aneurysms, specifically in keyhole and conventional clipping procedures. METHODS: The prospective study that was performed to examine the effects of aneurysm surgery on the patient's global neurocognitive status and frontal executive functions started on April 2016. After exclusion posterior circulation aneurysms, anterior communicating aneurysms treated by interhemispheric approach, giant aneurysms, and paraclinoid aneurysms, 23 patients who were enrolled before May 2017 were treated by conventional clipping, and 18 patients who were enrolled after June 2017 were treated by keyhole clipping. Two patients were excluded from each group due to missing data. Finally, 21 and 16 patients in each group were analyzed, respectively. Three-tesla magnetic resonance imaging was performed before and after surgery to detect the presence of perioperative cerebral infarctions and brain edema. The Mini-Mental State Examination, Frontal Assessment Battery, and Self-Rating Depression Scale scores were obtained before and 1 month after surgery. RESULTS: Logistic regression analyses indicated that anterior communicating and internal carotid artery aneurysms were the most significant factors for poor outcomes and that keyhole clipping for these two types of aneurysm was the most significant factor for the preservation of patient global neurocognitive status. Keyhole clipping was also the most significant factor for the preservation of frontal executive functions in patients. CONCLUSIONS: Keyhole clipping may be more favorable than conventional clipping for the preservation of the global neurocognitive status and frontal executive functions. Moreover, it may be the most effective factor for preservation of global neurocognitive status when it is indicated for anterior communicating or internal carotid artery aneurysms.

Prostate-specific antigen nomogram to predict advanced prostate cancer using area under the receiver operating characteristic curve boosting.

BACKGROUND: For prostate cancer, accurate prediction of the pathological stage before surgery is very important. Therefore, the aim of the present study was establishing the prostate-specific antigen (PSA) threshold nomogram to predict pathologically advanced prostate cancer using the novel method of area under the receiver operating characteristic curve boosting (AUCBoost). METHODS: The medical records of patients with clinically localized prostate cancer who underwent robot-assisted radical prostatectomy were retrospectively reviewed. Multivariate logistic regression analysis was performed to identify clinical covariates significantly associated with pathological tumor stage ≥3a. The best combination of the variables was determined by validated values of the area under the curve (AUC). The optimal individualized PSA threshold values were developed using AUCBoost. RESULTS: In the multivariate logistic regression analysis, PSA, prostate volume, clinical tumor stage, Gleason Grade Group, the number of positive cores, and the percentage of positive cores were independent predictive factors for pathological tumor stage ≥3a. A combination model comprising PSA, prostate volume, clinical tumor stage, percent positive core, and Gleason Grade Group produced the highest AUC for predicting pathological tumor stage ≥3a (AUC = 0.777). The PSA threshold values for detecting pathological tumor stage ≥3a were calculated and a table of individualized PSA threshold nomogram was developed using AUCBoost. CONCLUSIONS: We developed a nomogram of the PSA threshold values for predicting adverse pathological tumor stages of prostate cancer using a novel statistical method. Further validation is necessary; however, the individualized PSA threshold nomogram may be useful in determining treatment strategies before surgery.

The significance of micro-lymphatic invasion and pathological Gleason score in prostate cancer patients with pathologically organ-confined disease and negative surgical margins after robot-assisted radical prostatectomy.

BACKGROUND: The development process of recurrence in prostate cancer patients with pathologically organ-confined (pT2) disease and negative surgical margins is unclear. The aim of the present study was to determine factors associated with the development of biochemical recurrence following robot-assisted radical prostatectomy among those prostate cancer patients. METHODS: We retrospectively reviewed the data of patients who underwent robot-assisted radical prostatectomy without neoadjuvant endocrine therapy. We evaluated prognostic factors in 1096 prostate cancer patients with pT2 disease and negative surgical margins. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent predictors for biochemical recurrence. RESULTS: Of the 1096 patients, 55 experienced biochemical recurrence during the follow-up period. The 5-year biochemical recurrence-free survival rate for patients with pT2 and negative surgical margins was 91.8%. On univariate analysis, clinical stage, biopsy Gleason score, percent of positive core, pathological Gleason score, and the presence of micro-lymphatic invasion were significantly associated with biochemical recurrence. On a multivariate analysis, the presence of micro-lymphatic invasion and a pathological Gleason score ≥ 4 + 3 were significant prognostic factors for biochemical recurrence. Based on these factors, we developed a risk stratification model. The biochemical recurrence-free survival rate differed significantly among the risk groups. CONCLUSIONS: The prognosis of prostate cancer patients with pT2 disease and negative surgical margins is favorable. However, patients with the presence of micro-lymphatic invasion and a pathological Gleason score ≥ 4 + 3 tend to experience biochemical recurrence more often after surgery. Therefore, careful follow-up might be necessary for those patients.

Development of novel diagnostic system for pancreatic cancer, including early stages, measuring mRNA of whole blood cells.

Pancreatic ductal adenocarcinoma (PDAC) is the most life-threating disease among all digestive system malignancies. We developed a blood mRNA PDAC screening system using real-time detection PCR to detect the expression of 56 genes, to discriminate PDAC from noncancer subjects. We undertook a clinical study to assess the performance of the developed system. We collected whole blood RNA from 53 PDAC patients, 102 noncancer subjects, 22 patients with chronic pancreatitis, and 23 patients with intraductal papillary mucinous neoplasms in a per protocol analysis. The sensitivity of the system for PDAC diagnosis was 73.6% (95% confidence interval, 59.7%-84.7%). The specificity for noncancer volunteers, chronic pancreatitis, and patients with intraductal papillary mucinous neoplasms was 64.7% (54.6%-73.9%), 63.6% (40.7%-82.8%), and 47.8% (26.8%-69.4%), respectively. Importantly, the sensitivity of this system for both stage I and stage II PDAC was 78.6% (57.1%-100%), suggesting that detection of PDAC by the system is not dependent on the stage of PDAC. These results indicated that the screening system, relying on assessment of changes in mRNA expression in blood cells, is a viable alternative screening strategy for PDAC.

Quasi-linear score for capturing heterogeneous structure in biomarkers.

BACKGROUND: Linear scores are widely used to predict dichotomous outcomes in biomedical studies because of their learnability and understandability. Such approaches, however, cannot be used to elucidate biodiversity when there is heterogeneous structure in target population. RESULTS: Our study was focused on describing intrinsic heterogeneity in predictions. Because heterogeneity can be captured by a clustering method, integrating different information from different clusters should yield better predictions. Accordingly, we developed a quasi-linear score, which effectively combines the linear scores of clustered markers. We extended the linear score to the quasi-linear score by a generalized average form, the Kolmogorov-Nagumo average. We observed that two shrinkage methods worked well: ridge shrinkage for estimating the quasi-linear score, and lasso shrinkage for selecting markers within each cluster. Simulation studies and applications to real data show that the proposed method has good predictive performance compared with existing methods. CONCLUSIONS: Heterogeneous structure is captured by a clustering method. Quasi-linear scores combine such heterogeneity and have a better predictive ability compared with linear scores.

Preoperative predictive factors and further risk stratification of biochemical recurrence in clinically localized high-risk prostate cancer.

BACKGROUND: To identify preoperative predictive factors for biochemical recurrence (BCR) and to further stratify its risk in high-risk localized prostate cancer patients receiving radical prostatectomy (RP). METHODS: Subjects included 195 high-risk prostate cancer patients undergoing RP from 2000 to 2012. RP consisted of retropubic radical prostatectomy and robot-assisted radical prostatectomy, involving 84 cases and 111 cases, respectively. BCR was defined as a prostate serum antigen (PSA) level ≥0.2 ng/mL. BCR-free survival (BCRFS) was calculated using the Kaplan-Meier method. Preoperative predictors of BCR were assessed with Cox's proportional hazard regression analysis. RESULTS: Eighty-nine patients (45.6 %) experienced recurrence. BCRFS rates 3 and 5 years after RP were 58 and 50 %, respectively. Prostate volume, transition zone volume, and Gleason score were not significantly associated with BCR. Patients with higher preoperative PSA, PSA density (PSAD), PSA density of the transition zone, percentage of positive cores (PPC), and PPC from the dominant side showed significantly lower BCRFS. The PPC from the dominant side and PSAD were significant independent prognostic factors for BCR. Using these variables, the hazard ratio of BCR could be calculated and patients stratified into three risk groups. The 5-year BCRFS rates for Groups 1, 2, and 3 were 64.9 %, 48.1 %, and 21.3 %, respectively. CONCLUSIONS: Patients with high-risk localized prostate cancer as currently defined do not have uniformly poor prognosis after RP. PPC from the dominant side and PSAD are significant predictors of BCR. These factors can identify high-risk patients with very poor prognosis.

Generalized t-statistic for two-group classification.

In the classic discriminant model of two multivariate normal distributions with equal variance matrices, the linear discriminant function is optimal both in terms of the log likelihood ratio and in terms of maximizing the standardized difference (the t-statistic) between the means of the two distributions. In a typical case-control study, normality may be sensible for the control sample but heterogeneity and uncertainty in diagnosis may suggest that a more flexible model is needed for the cases. We generalize the t-statistic approach by finding the linear function which maximizes a standardized difference but with data from one of the groups (the cases) filtered by a possibly nonlinear function U. We study conditions for consistency of the method and find the function U which is optimal in the sense of asymptotic efficiency. Optimality may also extend to other measures of discriminatory efficiency such as the area under the receiver operating characteristic curve. The optimal function U depends on a scalar probability density function which can be estimated non-parametrically using a standard numerical algorithm. A lasso-like version for variable selection is implemented by adding L1-regularization to the generalized t-statistic. Two microarray data sets in the study of asthma and various cancers are used as motivating examples.

Individualized prostate-specific antigen threshold values to avoid overdiagnosis of prostate cancer and reduce unnecessary biopsy in elderly men.

OBJECTIVE: To individualize prostate-specific antigen threshold values to avoid overdiagnosis of prostate cancer and reduce unnecessary biopsy in elderly men. METHODS: A total of 406 men aged over 70 years old with prostate-specific antigen levels between 4.0 and 20.0 ng/ml, normal digital rectal examination results and diagnosed by transrectal needle biopsy were retrospectively analyzed. The patients were divided into a no/favorable-risk cancer group or an unfavorable-risk cancer group based on their Gleason score and the number of positive cores. Prostate-specific antigen levels, percent free prostate-specific antigen level, prostate transition zone volume and the number of previous biopsies were used to discriminate between the two groups. The optimal individualized prostate-specific antigen threshold values based on the other variables that gave a sensitivity of 95% for the detection of unfavorable-risk cancer were calculated using a boosting method for maximizing the area under the receiver operating characteristic curve. RESULTS: A total of 66 men had favorable-risk cancer, and 139 had unfavorable-risk cancer. The area under the receiver operating characteristic curve of the combination model determined by the boosting method for maximizing the area under the receiver operating characteristic curve was 0.852. The sensitivity and specificity of the threshold values for the detection of unfavorable-risk cancer were 95 and 36%, respectively. By using the threshold values, 100 (25%) of the subjects with no/favorable-risk cancer could have avoided undergoing biopsies, with a <5% risk of missing the detection of unfavorable-risk cancer. CONCLUSIONS: These individualized prostate-specific antigen threshold values may be useful for determining an indication of prostate biopsy for elderly men to avoid overdiagnosis of prostate cancer and reduce unnecessary biopsy.

Multiple suboptimal solutions for prediction rules in gene expression data.

This paper discusses mathematical and statistical aspects in analysis methods applied to microarray gene expressions. We focus on pattern recognition to extract informative features embedded in the data for prediction of phenotypes. It has been pointed out that there are severely difficult problems due to the unbalance in the number of observed genes compared with the number of observed subjects. We make a reanalysis of microarray gene expression published data to detect many other gene sets with almost the same performance. We conclude in the current stage that it is not possible to extract only informative genes with high performance in the all observed genes. We investigate the reason why this difficulty still exists even though there are actively proposed analysis methods and learning algorithms in statistical machine learning approaches. We focus on the mutual coherence or the absolute value of the Pearson correlations between two genes and describe the distributions of the correlation for the selected set of genes and the total set. We show that the problem of finding informative genes in high dimensional data is ill-posed and that the difficulty is closely related with the mutual coherence.