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Advances in Diagnosis and Therapy of Neuroendocrine Neoplasms
| Advances in Diagnosis and Therapy of Neuroendocrine Neoplasms |
| Autore | Segelov Eva |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (144 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
small bowel neuroendocrine tumours
pancreatic neuroendocrine tumours liver metastases midgut meta-analysis neuroendocrine tumors carcinoid heart disease carcinoid syndrome somatostatin analogues metastases multidisciplinary management outcome grading staging neuroendocrine neoplasms chemotherapy temozolomide metronomic treatment second-line NOTCH cancer-driven genes mutational mechanism germline mutations small cell lung carcinoma pancreatic NET small bowel NET medullary thyroid carcinoma malignant castration-resistant prostatic cells quality performance indicators QPIs cancer care neuroendocrine tumour NETs modified Delphi CommNETs pancreatic neuroendocrine neoplasms neuroendocrine tumor long-term functional outcomes pancreatectomy diabetes mellitus pancreatic exocrine insufficiency body mass index parenchyma-sparing surgery neuroendocrine tumours curative surgery resection follow-up guidelines relapse recurrence risk factor mixed non-neuroendocrine neuroendocrine neoplasms MiNENs mixed adeno-neuroendocrine carcinoma MANEC 2017 WHO classification 2019 WHO classification |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557692903321 |
Segelov Eva
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Application of Bioinformatics in Cancers
| Application of Bioinformatics in Cancers |
| Autore | Brenner J. Chad |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (418 p.) |
| Soggetto non controllato |
cancer treatment
extreme learning independent prognostic power AID/APOBEC HP gene inactivation biomarkers biomarker discovery chemotherapy artificial intelligence epigenetics comorbidity score denoising autoencoders protein single-biomarkers gene signature extraction high-throughput analysis concatenated deep feature feature selection differential gene expression analysis colorectal cancer ovarian cancer multiple-biomarkers gefitinib cancer biomarkers classification cancer biomarker mutation hierarchical clustering analysis HNSCC cell-free DNA network analysis drug resistance hTERT variable selection KRAS mutation single-cell sequencing network target skin cutaneous melanoma telomeres Neoantigen Prediction datasets clinical/environmental factors StAR PD-L1 miRNA circulating tumor DNA (ctDNA) false discovery rate predictive model Computational Immunology brain metastases observed survival interval next generation sequencing brain machine learning cancer prognosis copy number aberration mutable motif steroidogenic enzymes tumor mortality tumor microenvironment somatic mutation transcriptional signatures omics profiles mitochondrial metabolism Bufadienolide-like chemicals cancer-related pathways intratumor heterogeneity estrogen locoregionally advanced RNA feature extraction and interpretation treatment de-escalation activation induced deaminase knockoffs R package copy number variation gene loss biomarkers cancer CRISPR overall survival histopathological imaging self-organizing map Network Analysis oral cancer biostatistics firehose Bioinformatics tool alternative splicing biomarkers diseases genes histopathological imaging features imaging TCGA decision support systems The Cancer Genome Atlas molecular subtypes molecular mechanism omics curative surgery network pharmacology methylation bioinformatics neurological disorders precision medicine cancer modeling miRNAs breast cancer detection functional analysis biomarker signature anti-cancer hormone sensitive cancers deep learning DNA sequence profile pancreatic cancer telomerase Monte Carlo mixture of normal distributions survival analysis tumor infiltrating lymphocytes curation pathophysiology GEO DataSets head and neck cancer gene expression analysis erlotinib meta-analysis traditional Chinese medicine breast cancer TCGA mining breast cancer prognosis microarray DNA interaction health strengthening herb cancer genomic instability |
| ISBN | 3-03921-789-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367743403321 |
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Brenner J. Chad
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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