Search

Background: Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer. Methods: ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases. Results: Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women. Conclusions: ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up. [ABSTRACT FROM AUTHOR]

Background: Di(2-ethylhexyl) phthalate (DEHP) is one of the most widely used phthalates and is associated with breast cancer. Ths association between DEHP and other types of cancer is not clear. DEHP may increase matrix metalloproteinase-9 that is critical for the development of urothelial cancer (UC). We examined the association between urinary phthalate metabolites and UC. CKD patients were selected as a control group because CKD patients are more at risk of UC than the general population., Methods: In this cross-sectional study, we measured seven urinary phthalate metabolites that are abundant and can be measured using HPLC-MS/MS in Taiwan CKD patients between Jul 2013 and Dec 2015. MiBP (a urinary metabolite of Dibutyl phthalates[DBP]) and MEHHP (a urinary metabolite of DEHP) were described because they are the most abundant phthalate metabolites. The association of phthalate (log-transformed) and UC were analyzed using logistic regression with adjustments for age, gender, renal function, use of traditional Chinese medicine, toxins (dye, organic solvent), and non-steroidal anti-inflammatory drugs., Results: We measured the urinary MEHHP and MiBP of 496 patients (224 UC and 272 CKD patients). The urinary MEHHP was associated with UC but MiBP was not. Medical history including the use of non-steroid anti-inflammatory drugs, exposure to environmental toxins (dye, paint, and organic solvent), and the use of traditional Chinese medicine was independently associated with UC. The adjusted odds ratio of MEHHP was 1.42 (95% confidence interval: 1.21-1.68)., Conclusion: Phthalate urinary metabolite(MEHHP) may be associated with UC in CKD patients and the association is independent of well-known risk factors of UC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

Early detection is important for improving the survival rate of patients with gastric cancer (GC). Serum tumor markers have been widely used for detecting GC. However, their clinical values remain controversial. This study aims to investigate the role of serum cancer antigen 72-4 (CA72-4) in the diagnosis of GC in a healthy population. A total of 7757 adults who underwent upper gastrointestinal endoscopy and serum CA72-4 level measurement in multicenters in Taiwan from January 2006 to August 2016 were recruited in this retrospective study. Risk factors for GC, serum tumor markers, and esophagogastroduodenoscopy (EGD) findings were evaluated. High serum levels of CA72-4 were found in 7.2% of healthy adults. CA72-4 level showed lower sensitivity (33.3%) but higher specificity (92.8%); however, the positive predictive value was quite low (0.18%). After adjustment of clinical risk factors for GC using EGD findings, gastric ulcer (adjusted odds ratio (aOR) = 2.11), gastric polyps (aOR = 1.42), and atrophic gastritis (aOR = 1.27) were significantly associated with high serum CA72-4 levels. Furthermore, both age (OR = 1.01) and Helicobacter pylori infection (OR = 1.44) exhibited a significant association with high serum CA72-4 levels. These results indicate that routine screening of CA72-4 levels for diagnosing GC in asymptomatic patients may be ineffective due to low sensitivity and low positive predictive value. The clinical utility of EGD findings along with serum CA72-4 level for screening healthy individuals with GC is warranted. [ABSTRACT FROM AUTHOR]