Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction

Ottaiano A, Scala S, Santorsola M, Trotta AM, D’Alterio C, Portella L, Clemente O, Nappi A, Zanaletti N, De Stefano A, Avallone A, Granata V, Notariello C, Luce A, Lombardi A, Picone C, Petrillo A, Perri F, Tatangelo F, Di Mauro A, Albino V, Izzo F, Rega D, Pace U, Di Marzo M, Chiodini P, De Feo G, Del Prete P, Botti G, Delrio P, Caraglia M, Nasti G (2021) Aflibercept or bevacizumab in combination with FOLFIRI as second-line treatment of mRAS metastatic colorectal cancer patients: the ARBITRATION study protocol. Ther Adv Med Oncol 24(13):1758835921989223. https://doi.org/10.1177/1758835921989223

Article  CAS  Google Scholar 

Ottaiano A, Caraglia M, Di Mauro A, Botti G, Lombardi A, Galon J, Luce A, D’Amore L, Perri F, Santorsola M, Hermitte F, Savarese G, Tatangelo F, Granata V, Izzo F, Belli A, Scala S, Delrio P, Circelli L, Nasti G (2020) Evolution of mutational landscape and tumor immune-microenvironment in liver oligo-metastatic colorectal cancer. Cancers (Basel) 12(10):3073. https://doi.org/10.3390/cancers12103073

Article  CAS  PubMed  Google Scholar 

Cervantes A, Adam R, Roselló S, Arnold D, Normanno N, Taïeb J, Seligmann J, De Baere T, Osterlund P, Yoshino T, Martinelli E, ESMO Guidelines Committee (2023) Electronic address: clinicalguidelines@esmo.org. Metastatic colorectal cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol 34(1):10–32. https://doi.org/10.1016/j.annonc.2022.10.003

Article  CAS  PubMed  Google Scholar 

Mateo J, Steuten L, Aftimos P, André F, Davies M, Garralda E, Geissler J, Husereau D, Martinez-Lopez I, Normanno N, Reis-Filho JS, Stefani S, Thomas DM, Westphalen CB, Voest E (2022) Delivering precision oncology to patients with cancer. Nat Med 28(4):658–665. https://doi.org/10.1038/s41591-022-01717-2

Article  CAS  PubMed  Google Scholar 

Mosele F, Remon J, Mateo J, Westphalen CB, Barlesi F, Lolkema MP, Normanno N, Scarpa A, Robson M, Meric-Bernstam F, Wagle N, Stenzinger A, Bonastre J, Bayle A, Michiels S, Bièche I, Rouleau E, Jezdic S, Douillard JY, Reis-Filho JS, Dienstmann R, André F (2020) Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO precision medicine working group. Ann Oncol 31(11):1491–1505. https://doi.org/10.1016/j.annonc.2020.07.014

Article  CAS  PubMed  Google Scholar 

Martinelli E, Ciardiello D, Martini G, Troiani T, Cardone C, Vitiello PP, Normanno N, Rachiglio AM, Maiello E, Latiano T, De Vita F, Ciardiello F (2020) Implementing anti-epidermal growth factor receptor (EGFR) therapy in metastatic colorectal cancer: challenges and future perspectives. Ann Oncol 31(1):30–40. https://doi.org/10.1016/j.annonc.2019.10.007

Article  CAS  PubMed  Google Scholar 

Ottaiano A, Sabbatino F, Perri F, Cascella M, Sirica R, Patrone R, Capuozzo M, Savarese G, Ianniello M, Petrillo N, Circelli L, Granata V, Berretta M, Santorsola M, Nasti G (2023) KRAS p.G12C mutation in metastatic colorectal cancer: prognostic implications and advancements in targeted therapies. Cancers (Basel) 15(14):3579. https://doi.org/10.3390/cancers15143579

Article  CAS  PubMed  Google Scholar 

Collienne M, Neven A, Caballero C, Kataoka K, Carrion-Alvarez L, Nilsson H, Désolneux G, Rivoire M, Ruers T, Gruenberger T, Protic M, Troisi RI, Primavesi F, Staettner S, Rahbari N, Schnitzbauer A, Malik H, Swijnenburg RJ, Mauer M, Ducreux M, Evrard S (2023) EORTC 1409 GITCG/ESSO 01–A prospective colorectal liver metastasis database for borderline or initially unresectable diseases (CLIMB): lessons learnt from real life. From paradigm to unmet need. Eur J Surg Oncol. 49(11):107081. https://doi.org/10.1016/j.ejso.2023.107081

Article  PubMed  Google Scholar 

Vauthey JN, Zimmitti G, Kopetz SE, Shindoh J, Chen SS, Andreou A, et al. (2013) Ras mutation status predicts survival and patterns of recurrence in patients undergoing hepatectomy for colorectal liver metastases. Ann Surg 258(4):619–26. discussion 26–7

Margonis GA, Sasaki K, Gholami S, Kim Y, Andreatos N, Rezaee N et al (2018) Genetic and morphological evaluation (game) score for patients with colorectal liver metastases. Br J Surg 105(9):1210–1220

Article  CAS  PubMed  Google Scholar 

Brudvik KW, Jones RP, Giuliante F, Shindoh J, Passot G, Chung MH et al (2019) Ras mutation clinical risk score to predict survival after resection of colorectal liver metastases. Ann Surg 269(1):120–126

Article  PubMed  Google Scholar 

Lambin P, Rios-Velazquez E, Leijenaar R, Carv-alho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

Article  PubMed  PubMed Central  Google Scholar 

Dercle L, Lu L, Schwartz LH, Qian M, Tejpar S, Eggleton P, Zhao B, Piessevaux H (2020) Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway. J Natl Cancer Inst 112(9):902–912. https://doi.org/10.1093/jnci/djaa017

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A (2022) Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 19(2):132–146. https://doi.org/10.1038/s41571-021-00560-7

Article  CAS  PubMed  Google Scholar 

Taghavi M, Trebeschi S, Simões R, Meek DB, Beckers RCJ, Lambregts DMJ, Verhoef C, Houwers JB, van der Heide UA, Beets-Tan RGH, Maas M (2021) Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY) 46(1):249–256. https://doi.org/10.1007/s00261-020-02624-1

Article  PubMed  Google Scholar 

Granata V, Fusco R, Barretta ML, Picone C, Avallone A, Belli A, Patrone R, Ferrante M, Cozzi D, Grassi R, Grassi R, Izzo F, Petrillo A (2021) Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 16(1):39. https://doi.org/10.1186/s13027-021-00379-y

Article  PubMed  PubMed Central  Google Scholar 

Petrillo A, Fusco R, Barretta ML, Granata V, Mattace Raso M, Porto A, Sorgente E, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Trombadori CML, Belli P, Trecate G, Tenconi C, De Santis MC, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Boldrini L (2023) Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict breast cancer histological outcome. Radiol Med. https://doi.org/10.1007/s11547-023-01718-2

Article  PubMed  Google Scholar 

Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A (2023) Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. https://doi.org/10.1007/s11547-023-01710-w

Article  PubMed  PubMed Central  Google Scholar 

Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell’Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A (2022) Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 127(7):763–772. https://doi.org/10.1007/s11547-022-01501-9

Article  PubMed  Google Scholar 

Granata V, Fusco R, De Muzio F, Cutolo C, Mattace Raso M, Gabelloni M, Avallone A, Ottaiano A, Tatangelo F, Brunese MC, Miele V, Izzo F, Petrillo A (2022) Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of colorectal liver metastases growth pattern. Diagn (Basel) 12(5):1115. https://doi.org/10.3390/diagnostics12051115

Article  Google Scholar 

Granata V, Fusco R, Setola SV, De Muzio F, Dell’ Aversana F, Cutolo C, Faggioni L, Miele V, Izzo F, Petrillo A (2022) CT-based radiomics analysis to predict histopathological outcomes following liver resection in colorectal Liver metastases. Cancers (Basel) 14(7):1648. https://doi.org/10.3390/cancers14071648

Article  CAS  PubMed  PubMed Central  Google Scholar 

Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Grassi R, Grassi F, Ottaiano A, Nasti G, Tatangelo F, Pilone V, Miele V, Brunese MC, Izzo F, Petrillo A (2022) Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol Med 127(5):461–470. https://doi.org/10.1007/s11547-022-01477-6

Article  PubMed  Google Scholar 

Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell’Aversana F, Ottaiano A, Nasti G, Grassi R, Pilone V, Miele V, Brunese MC, Tatangelo F, Izzo F, Petrillo A (2022) EOB-MR based radiomics analysis to assess clinical outcomes following liver resection in colorectal liver metastases. Cancers (Basel) 14(5):1239. https://doi.org/10.3390/cancers14051239

Article  PubMed  PubMed Central  Google Scholar 

Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell’ Aversana F, Ottaiano A, Avallone A, Nasti G, Grassi F, Pilone V, Miele V, Brunese L, Izzo F, Petrillo A (2022) Contrast MR-based radiomics and machine learning analysis to assess clinical outcomes following liver resection in colorectal liver metastases: a preliminary study. Cancers (Basel) 14(5):1110. https://doi.org/10.3390/cancers14051110

Article  PubMed  PubMed Central  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillon-Robin JC, Pieper S, Aerts HJWL (2017) Computational radiomics system to decode the radiographic phenotype. Can Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

Article  CAS  Google Scholar 

Kocak B, Akinci D’Antonoli T, Mercaldo N et al (2024) METhodological Radiomics Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15:8. https://doi.org/10.1186/s13244-023-01572-w

Article  PubMed  PubMed Central  Google Scholar 

Kocak B, Baessler B, Bakas S, Cuocolo R, Fedorov A, Maier-Hein L, Mercaldo N, Müller H, Orlhac F, Pinto Dos Santos D, Stanzione A, Ugga L, Zwanenburg A (2023) CheckList for evaluation of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14(1):75. https://doi.org/10.1186/s13244-023-01415-8

Article  PubMed  PubMed Central  Google Scholar 

Chen Z, Lin T, Xia X, Xu H, Ding S (2017) A synthetic neighborhood generation based ensemble learning for the imbalanced data classification. Appl Intell 48:2441–2457

Article  Google Scholar 

Tibshirani R (1996) Regression shrinkage and selection Via the Lasso. J R Stat Soc Ser B Statist Methodol. 58:267–288

Article  Google Scholar 

Granata V, Fusco R, Avallone A, De Stefano A, Ottaiano A, Sbordone C, Brunese L, Izzo F, Petrillo A (2021) Radiomics-derived data by contrast enhanced magnetic resonance in RAS mutations detection in colorectal liver metastases. Cancers (Basel) 13(3):453. https://doi.org/10.3390/cancers13030453

Article  PubMed  Google Scholar 

Granata V, Fusco R, Risi C, Ottaiano A, Avallone A, De Stefano A, Grimm R, Grassi R, Brunese L, Izzo F, Petrillo A (2020) Diffusion-weighted MRI and di

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