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Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer.
Osman, Sarah O S; Leijenaar, Ralph T H; Cole, Aidan J; Lyons, Ciara A; Hounsell, Alan R; Prise, Kevin M; O'Sullivan, Joe M; Lambin, Philippe; McGarry, Conor K; Jain, Suneil.
Afiliação
  • Osman SOS; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom. Electronic address: s.osman@qub.ac.uk.
  • Leijenaar RTH; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands.
  • Cole AJ; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
  • Lyons CA; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
  • Hounsell AR; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
  • Prise KM; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom.
  • O'Sullivan JM; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
  • Lambin P; The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands.
  • McGarry CK; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
  • Jain S; Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
Int J Radiat Oncol Biol Phys ; 105(2): 448-456, 2019 10 01.
Article em En | MEDLINE | ID: mdl-31254658
ABSTRACT

PURPOSE:

To explore the role of Computed tomography (CT)-based radiomics features in prostate cancer risk stratification. METHODS AND MATERIALS The study population consisted of 506 patients with prostate cancer collected from a clinically annotated database. After applying exclusion criteria, 342 patients were included in the final analysis. CT-based radiomics features were extracted from planning CT scans for prostate gland-only structure, and machine learning was used to train models for Gleason score (GS) and risk group (RG) classifications. Repeated cross-validation was used. The discriminatory performance of the developed models was assessed using receiver operating characteristic area under the curve (AUC) analysis.

RESULTS:

Classifiers using CT-based radiomics features distinguished between GS ≤ 6 versus GS ≥ 7 with AUC = 0.90 and GS 7(3 + 4) versus GS 7(4 + 3) with AUC = 0.98. Developed classifiers also showed excellent performance in distinguishing low versus high RG (AUC = 0.96) and low versus intermediate RG (AUC = 1.00), but poorer performance was observed for GS 7 versus GS > 7 (AUC = 0.69). An overall modest performance was observed for validation on holdout data sets with the highest AUC of 0.75 for classifiers of low versus high RG and an AUC of 0.70 for GS 7 versus GS > 7.

CONCLUSIONS:

Our results show that radiomics features from routinely acquired planning CT scans could provide insights into prostate cancer aggressiveness in a noninvasive manner. Assessing models on training data sets, the classifiers were especially accurate in discerning high-risk from low-risk patients and in classifying GS 7 versus GS > 7 and GS 7(3 + 4) versus G7(4 + 3); however, classifiers were less adept at distinguishing high RG versus intermediate RG. External validation and prospective studies are warranted to verify the presented findings. These findings could potentially guide targeted radiation therapy strategies in radical intent radiation therapy for prostate cancer.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Tomografia Computadorizada por Raios X Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Tomografia Computadorizada por Raios X Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article