Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features

  • V. GianniniEmail author
  • S. Mazzetti
  • I. Bertotto
  • C. Chiarenza
  • S. Cauda
  • E. Delmastro
  • C. Bracco
  • A. Di Dia
  • F. Leone
  • E. Medico
  • A. Pisacane
  • D. Ribero
  • M. Stasi
  • D. Regge
Original Article



Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15–30% of cases, therefore it would be useful to assess if pretreatment of 18F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.


Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models.


In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7% and specificity = 76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86%, and specificity = 83% at the Youden index).


If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.


Locally advanced rectal cancer 18F-FDG PET/CT imaging Magnetic resonance imaging Texture features Prediction of treatment response Radiomics 



This work was funded by “AIRC 5xmille Special Program Molecular Clinical Oncology - Ref. 9970” and “FPRC 5xmille 2013 Ministero Salute”.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • V. Giannini
    • 1
    • 2
    Email author
  • S. Mazzetti
    • 1
    • 2
  • I. Bertotto
    • 1
  • C. Chiarenza
    • 1
  • S. Cauda
    • 3
  • E. Delmastro
    • 4
  • C. Bracco
    • 5
  • A. Di Dia
    • 5
  • F. Leone
    • 6
  • E. Medico
    • 7
  • A. Pisacane
    • 8
  • D. Ribero
    • 9
  • M. Stasi
    • 5
  • D. Regge
    • 1
    • 2
  1. 1.Imaging UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  2. 2.Department of Surgical SciencesUniversity of TurinTurinItaly
  3. 3.Nuclear Medicine UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  4. 4.Radiation Therapy UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  5. 5.Medical Physics UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  6. 6.Medical Oncology UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  7. 7.Laboratory of OncogenomicsCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  8. 8.Pathology UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly
  9. 9.Hepatobilio-Pancreatic and Colorectal Surgery UnitCandiolo Cancer Institute, FPO-IRCCSCandioloItaly

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