BACKGROUND: Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. OBJECTIVE: To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. METHODS: Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. RESULTS: This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. CONCLUSION: Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.
BACKGROUND:Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. OBJECTIVE: To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. METHODS: Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. RESULTS: This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. CONCLUSION: Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.
Authors: M Wintermark; M Sesay; E Barbier; K Borbély; W P Dillon; J D Eastwood; T C Glenn; C B Grandin; S Pedraza; J F Soustiel; T Nariai; G Zaharchuk; J M Caillé; V Dousset; H Yonas Journal: J Neuroradiol Date: 2005-12 Impact factor: 3.447
Authors: N Shastry Akella; Donald B Twieg; Tom Mikkelsen; Fred H Hochberg; Stuart Grossman; Gretchen A Cloud; L Burt Nabors Journal: J Magn Reson Imaging Date: 2004-12 Impact factor: 4.813
Authors: Roger Stupp; Warren P Mason; Martin J van den Bent; Michael Weller; Barbara Fisher; Martin J B Taphoorn; Karl Belanger; Alba A Brandes; Christine Marosi; Ulrich Bogdahn; Jürgen Curschmann; Robert C Janzer; Samuel K Ludwin; Thierry Gorlia; Anouk Allgeier; Denis Lacombe; J Gregory Cairncross; Elizabeth Eisenhauer; René O Mirimanoff Journal: N Engl J Med Date: 2005-03-10 Impact factor: 91.245
Authors: S W Lee; B A Fraass; L H Marsh; K Herbort; S S Gebarski; M K Martel; E H Radany; A S Lichter; H M Sandler Journal: Int J Radiat Oncol Biol Phys Date: 1999-01-01 Impact factor: 7.038
Authors: E D H Gates; J S Lin; J S Weinberg; S S Prabhu; J Hamilton; J D Hazle; G N Fuller; V Baladandayuthapani; D T Fuentes; D Schellingerhout Journal: AJNR Am J Neuroradiol Date: 2020-02-06 Impact factor: 3.825
Authors: Christos Davatzikos; Jill S Barnholtz-Sloan; Spyridon Bakas; Rivka Colen; Abhishek Mahajan; Carmen Balaña Quintero; Jaume Capellades Font; Josep Puig; Rajan Jain; Andrew E Sloan; Chaitra Badve; Daniel S Marcus; Yoon Seong Choi; Seung-Koo Lee; Jong Hee Chang; Laila M Poisson; Brent Griffith; Adam P Dicker; Adam E Flanders; Thomas C Booth; Saima Rathore; Hamed Akbari; Chiharu Sako; Michel Bilello; Gaurav Shukla; Anahita Fathi Kazerooni; Steven Brem; Robert Lustig; Suyash Mohan; Stephen Bagley; MacLean Nasrallah; Donald M O'Rourke Journal: Neuro Oncol Date: 2020-06-09 Impact factor: 12.300