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Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study.
Boldrini, Luca; Chiloiro, Giuditta; Cusumano, Davide; Yadav, Poonam; Yu, Gao; Romano, Angela; Piras, Antonio; Votta, Claudio; Placidi, Lorenzo; Broggi, Sara; Catucci, Francesco; Lenkowicz, Jacopo; Indovina, Luca; Bassetti, Michael F; Yang, Yingli; Fiorino, Claudio; Valentini, Vincenzo; Gambacorta, Maria Antonietta.
Afiliação
  • Boldrini L; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Chiloiro G; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Cusumano D; Mater Olbia Hospital, Olbia, Sassari, Italy. davide.cusumano@materolbia.com.
  • Yadav P; Northwestern Memorial Hospital, Northwestern University Feinberg, Chicago, IL, USA.
  • Yu G; Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
  • Romano A; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Piras A; UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
  • Votta C; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Placidi L; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Broggi S; Medical Physics, San Raffaele Scientific Institute, Milan, Italy.
  • Catucci F; Mater Olbia Hospital, Olbia, Sassari, Italy.
  • Lenkowicz J; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Indovina L; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Bassetti MF; Department of Human Oncology, School of Medicine and Public Heath, University of Wisconsin - Madison, Madison, USA.
  • Yang Y; Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
  • Fiorino C; Medical Physics, San Raffaele Scientific Institute, Milan, Italy.
  • Valentini V; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
  • Gambacorta MA; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy.
Radiol Med ; 129(4): 615-622, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38512616
ABSTRACT

PURPOSE:

The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT.

METHODS:

Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves.

RESULTS:

A total of 91 patients was enrolled 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set).

CONCLUSION:

The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Retais / Radiômica Limite: Humans Idioma: En Revista: Radiol Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Retais / Radiômica Limite: Humans Idioma: En Revista: Radiol Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália