In the present study, we have elaborated a prediction model of short-term survival with high predictive capacity using the radiomic features of the structural preoperative multiparametric MRI of GBM patients.
We believe that the main strength of our study is based on a selection of patients who underwent a total or near-total resection of the enhancing tumor. We considered this methodologic aspect due to the undeniable link between the extent of resection and survival in these patients.36,37 In most previous studies, the extent of resection is not used as a selection criterion, including partial resections and biopsies in their series, without making any adjustment during the analysis phase. The exception is the studies by Bakas et al.15 and Fathi et al.38, in which the entire cohort of patients has undergone complete resection and standard chemoradiotherapy treatment.
Another crucial point of our work is to set our objective to identify short-term survival patients, in contrast to previously published studies where 10 and 12–15 months are used as cutpoints for defining short and long-term survival respectively.10,11,13,39−44 The only reference we found is in the work of Prasanna et al.45, who classify patients in long (> 18 months) versus short-term (< 7 months) survival based on peritumoral region radiomic features. The rationale of our approach lies in the desire to predict the survival of patients diagnosed with GBM by non-invasive methods and to identify those with very short survival. In these patients, the futility of our treatments would lead us to offer patients and their families the option of not taking aggressive measures or, on the contrary, opening new lines of research since those cases would be poor responders to the standard therapies applied currently.
As another strength of our work, we can mention the use of open-source software. The CapTk and Orange programs have a very intuitive yet robust user interface, thanks to which clinicians can access advanced image processing technics and data mining tools. Thanks to these programs, we have performed complex tasks such as automatic tumor segmentation, image processing, radiomic feature extraction, and exploring different ML-based algorithms.
Concerning statistical analysis, we have used a dual approach. On the one hand, we have used a binary classification system using different ML-based algorithms. Additionally, we have used state-of-the-art survival analysis techniques such as Random Survival Forest and time-dependent ROC curve analysis focused on short-term survival that contribute to corroborate the stability of the models produced here.
We also highlight that the results of our predictive models have been achieved using only structural MRI.15 These results could even be improved after the inclusion of studies based on diffusion and perfusion sequences.46 However, basic MRI is available in most centers, and according to our results, the lack of special sequences is not a limitation in the search for useful radiological patterns in clinical practice.
An important aspect to discuss is the biological correspondence of the variables employed by the prediction models. There is notable variability concerning the radiomic characteristics used by previous studies, which is one of the most significant obstacles in reproducing and validating their results. In our study, most of the selected variables come from the T1CE sequence followed by FLAIR and T2WI, while the different tumor sub-regions (i.e., ET, NET and ED) are represented in the models in a balanced way. In our series, first-order features and morphological characteristics appeared to be important for OS prediction.
We are aware of the limitations of our work, such as the lack of clinical and molecular data that can be incorporated into predictive models. Even so, age as an explanatory variable has been incorporated into our models due to its significant association with the OS of these patients, proving that its mere incorporation into the analysis allows improving the performance of the models. Despite having a relatively small sample size, various statistical techniques have been applied to overcome the "curse of dimensionality". Taking into consideration that MRI studies come from numerous sources, the processing method for image standardization that we have chosen aims to be simple and at the same time reliable and has been used by several studies.38,47,48
Unquestionably, the combination of texture analysis and artificial intelligence is starting to facilitate the knowledge about the biological behavior of GBM’s through the study of their patient-dependent heterogeneity. However, the rapid development of big data tools and the tremendous complexity of advanced medical image analysis dangerously threatens to widen the gap between data experts and clinicians. Then, it is a paradox that radiomics, defined by Lambin et al.35 as "the bridge between medical imaging and personalized medicine", is now out of reach of those who treat real patients every day. Therefore, our study arises from a real need and aims to find a solution to a clinically relevant problem: identifying GBM patients with short survival after complete resection. Although our results can be improved, we show that there are currently computer tools and public data sets available to everyone to develop reliable predictive models. Hence, our duty as clinicians is to get immersed in developing these models since our pragmatism can never be replaced even by the most complex algorithm.
Indeed, our results are encouraging, and the precision achieved is similar to the previous literature. However, this article represents an early age of a promising future in which the ultimate link between image, diagnosis and prognosis could finally be decoded in order to provide instant, useful and precise information to individual patients based on their specific features. Multi-institutional studies49 would allow the generalization of predictive models or even adapt the mechanisms of data pre-processing, extraction, and analysis to the MRI from each center since the standardization of acquisition protocols is not feasible. Finally, we believe that in this catastrophic disease, the quality of life of our patients should be our first consideration, and maximum exploitation of available neuroimaging techniques should be pursued to optimize management strategies avoiding unnecessarily aggressive therapies in those patients who won’t benefice from them.