Dataset Information

Prechemotherapy Not Preorchiectomy Serum Tumor Markers Accurately Identify International Germ Cell Cancer Collaborative Group Prognostic Groups in Nonseminoma.

ABSTRACT: Levels of the serum tumor markers (STMs) α-fetoprotein, human chorionic gonadotropin, and lactate dehydrogenase are used in staging classification for metastatic germ-cell cancers and support decisions on the intensity of first-line treatment for patients with nonseminoma. Use of preorchiectomy instead of prechemotherapy STM levels can lead to inadequate classification. We identified 744 men with metastatic gonadal nonseminoma in the International Germ-Cell Cancer Collaborative Group (IGCCCG) Update Consortium database who had preorchiectomy and prechemotherapy STM levels available. Of these, 22% would have had inadequate IGCCCG prognostic group classification if preorchiectomy levels had been used, which would have resulted in overtreatment of 16% and undertreatment of 6% of men. These findings suggest that use of preorchiectomy instead of prechemotherapy STM results may lead to incorrect IGCCCG classification, which could compromise treatment success or expose patients to unnecessary toxicity.

Patient summary

For men with testicular cancer, levels of tumor markers in their blood are used when making decisions on chemotherapy intensity. Use of test results for samples taken before removal of the cancer-bearing testicle instead of immediately before chemotherapy can lead to inadequate treatment recommendations.

SUBMITTER: Fankhauser CD

PROVIDER: S-EPMC10497784 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Prechemotherapy Not Preorchiectomy Serum Tumor Markers Accurately Identify International Germ Cell Cancer Collaborative Group Prognostic Groups in Nonseminoma.

Fankhauser Christian D CD Jandari Abolghassem A Collette Laurence L Tandstad Torgrim T Jiang Di Maria DM De Giorgi Ugo U Sweeney Christopher C Terbuch Angelika A Chovanec Michal M Huddart Robert R Bokemeyer Carsten C Beyer Jörg J Gillessen Silke S

European urology open science 20230909

Levels of the serum tumor markers (STMs) α-fetoprotein, human chorionic gonadotropin, and lactate dehydrogenase are used in staging classification for metastatic germ-cell cancers and support decisions on the intensity of first-line treatment for patients with nonseminoma. Use of preorchiectomy instead of prechemotherapy STM levels can lead to inadequate classification. We identified 744 men with metastatic gonadal nonseminoma in the International Germ-Cell Cancer Collaborative Group (IGCCCG) Up ...[more]

PMID: 37711670

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Project description:BackgroundIdentifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes' morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903.MethodsA total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms.ResultsFor both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified.ConclusionThese results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity.

Dataset Information

Prechemotherapy Not Preorchiectomy Serum Tumor Markers Accurately Identify International Germ Cell Cancer Collaborative Group Prognostic Groups in Nonseminoma.

Patient summary

Publications

Prechemotherapy Not Preorchiectomy Serum Tumor Markers Accurately Identify International Germ Cell Cancer Collaborative Group Prognostic Groups in Nonseminoma.

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