Objective: The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI. Methods: In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method. Results: Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89). Conclusion Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

Purpose: Hereditary parkinsonism genes consist of causative genes of familial Parkinson’s disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level. Materials and Methods: Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature. Results: Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant. Conclusion Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

PURPOSE: Few efforts have been made to integrate a next generation sequencing (NGS) panel into standard clinical treatment of ovarian cancer. The aim of this study was to investigate the clinical utility of NGS and to identify clinically impactful information beyond targetable alterations. MATERIALS AND METHODS: We conducted a retrospective review of 84 patients with ovarian cancer who underwent NGS between March 1, 2017, and July 31, 2018, at the Yonsei Cancer Hospital. We extracted DNA from formalin-fixed, paraffin-embedded tissue samples of ovarian cancer. The TruSight Tumor 170 gene panel was used to prepare libraries, and the MiSeq instrument was used for NGS. RESULTS: Of the 84 patients, 55 (65.1%) had high-grade serous carcinomas. Seventy-three (86.7%) patients underwent NGS at the time of diagnosis, and 11 (13.3%) underwent NGS upon relapse. The most common genetic alterations were in TP53 (64%), PIK3CA (15%), and BRCA1/2 (13%), arising as single nucleotide variants and indels. MYC amplification (27%) was the most common copy number variation and fusion. Fifty-seven (67.9%) patients had more than one actionable alteration other than TP53. Seven (8.3%) cases received matched-target therapy based on the following sequencing results: BRCA1 or 2 mutation, poly ADP ribose polymerase inhibitor (n=5); PIK3CA mutation, AKT inhibitor (n=1); and MLH1 mutation, PD-1 inhibitor (n=1). Fifty-three (63.0%) patients had a possibility of treatment change, and 8 (9.5%) patients received genetic counseling. CONCLUSION: Implementation of NGS may help in identifying patients who might benefit from targeted treatment therapies and genetic counseling.
Cancer Care Facilities ; Diagnosis ; DNA ; Genetic Counseling ; Humans ; Ovarian Neoplasms ; Poly(ADP-ribose) Polymerases ; Recurrence ; Retrospective Studies

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. Methods: In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. Results: The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). Conclusion Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

OBJECTIVE: The chemotherapy response score (CRS) system based on histopathological examination has been recently proposed for tubo-ovarian high-grade serous carcinoma (HGSC) to assess response to neoadjuvant chemotherapy (NAC). This study was aimed at validating the CRS system in an external cohort of tubo-ovarian HGSC patients. METHODS: This study included 110 tubo-ovarian HGSC patients who underwent NAC followed by interval debulking surgery. The 3-tiered CRS of the omental and adnexal tissue sections was determined by 3 independent pathologists. Differences in patient outcomes according to CRS were analyzed. RESULTS: The CRS system was highly reproducible among the 3 pathologists. Fleiss' kappa value and Kendall's coefficient of concordance for the omental CRS were 0.656 and 0.669, respectively. The omental CRS significantly predicted progression-free survival (PFS). The median PFS of patients whose tumors exhibited the omental CRS 1–2 (15 months) was significantly shorter than that of patients with an omental CRS of 3 (19 months; p=0.016). In addition, after adjusting for age, stage, and debulking status, the omental CRS was an independent prognostic factor for PFS of tubo-ovarian HGSC patients who were treated with NAC (adjusted hazard ratio [HR]=1.74; 95% confidence interval [CI]=1.05–2.87). CONCLUSION: The CRS system for assessing NAC response was a reproducible prognostic tool in our cohort. The application of the CRS system after NAC can improve survival estimation in HGSC patients.
Cohort Studies ; Disease-Free Survival ; Drug Therapy* ; Humans ; Ovarian Neoplasms

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.