Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients

Peeken, Jan C.; Hesse, Josefine; Haller, Bernhard; Kessel, Kerstin A.; Nüsslin, Fridtjof; Combs, Stephanie E.

doi:10.1007/s00066-018-1276-4

Cited by 42 publications

(38 citation statements)

References 39 publications

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“…Multiple previous studies have analyzed the prognostic potential of semantic MRI‐based features alone or in conjunction with clinical or pathological features. A simple model based only on three semantic imaging features "volume," "T1/FLAIR‐ratio," and "hemorrhage" achieved a 12‐month AUC of 0.67 for survival inferior to the combined models M6 and M7 (AUCs of 0.75 and 0.80, respectively) and similar to the clinical normogram by Gittleman et al Two further studies could demonstrate an incremental benefit by combining clinical features with VASARI features yielding a C‐index of 0.69, respectively . In our study, model M6 combining MRI‐based features with clinical, pathological and FET PET/CT‐based features showed a similar performance of 0.69.…”

Section: Discussionsupporting

confidence: 73%

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken

Goldberg²,

Pyka

et al. 2018

Cancer Medicine

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Background For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression‐free survival (PFS) on the basis of clinical, pathological, semantic MRI‐based, and FET‐PET/CT‐derived information. Finally, the value of adding treatment features was evaluated. Methods One hundred and eighty‐nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET‐PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI‐based," and "FET‐PET/CT‐based" models, as well as combinations. Treatment features were combined with all other features. Results Of all single feature class models, the MRI‐based model had the highest prediction performance on the validation set for OS (C‐index: 0.61 [95% confidence interval: 0.51‐0.72]) and PFS (C‐index: 0.61 [0.50‐0.72]). The combination of all features did increase performance above all single feature class models up to C‐indices of 0.70 (0.59‐0.84) and 0.68 (0.57‐0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C‐indices of 0.73 (0.62‐0.84) and 0.71 (0.60‐0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. Conclusions MRI‐based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

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Section: Discussionsupporting

confidence: 73%

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Peeken

Goldberg²,

Pyka

et al. 2018

Cancer Medicine

Self Cite

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“…A total of 59 models were presented of which the best performing model was included in this review. Two included models used the same database [14,36]. Yet, both were included in this systematic review because different predictors and algorithms were used to develop the models.…”

Section: Resultsmentioning

confidence: 99%

“…The utilized data sources were clinical parameters (n = 2) [19,32]; genomics (n = 2) [12,21]; MRI imaging (n = 4) [22,23,34,37]; combined clinical and genomics (n = 4) [13,16,27,28]; combined clinical and MRI imaging (n = 10) [14, 18, 20, 24, 25, 29-31, 35, 38]; combined clinical, MRI imaging, and genomics (n = 3) [15,26,36]; histopathology (n = 1) [33]; and combined clinical and pharmacokinetics (n = 1) [17]. Up to 2017, only two studies analyzed high-dimensional data sources (i.e., MRI or genomic information) in addition to clinical information [12,13].…”

Section: Type Of Inputmentioning

confidence: 99%

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Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

et al. 2020

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Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58–0.98), accuracy (0.69–0.98), and C-index (0.66–0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion.

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“…Accumulating evidence also suggests the predictive value of an MRI-based radiomics model in nasopharyngeal carcinoma, breast cancer, glioma and cervical cancer. [29][30][31][32][33][34] Given the widespread application and predictive value of MRI, it is necessary to build an MRI-based prognosis model for treatment guidance in resectable HCC.…”

Section: Discussionmentioning

confidence: 99%

MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

et al. 2020

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BACKGROUND: Recurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is difficult to select candidates for additional therapy. METHODS: A total of 201 patients with HCC who were followed up for at least 5 years after curative hepatectomy were enrolled in this retrospective, multicentre study. A total of 3144 radiomics features were extracted from preoperative MRI. The random forest method was used for radiomics signature building, and five-fold cross-validation was applied. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. RESULTS: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on the 5-year survival after surgery. The 30 most survival-related radiomics features were selected for the radiomics signature. Preoperative AFP and AST were integrated into the model as independent clinical risk factors. The model demonstrated good calibration and satisfactory discrimination, with a mean AUC of 0.9804 and 0.7578 in the training and validation sets, respectively. CONCLUSIONS: This radiomics model is a valid method to predict 5-year survival in patients with HCC and may be used to identify patients for clinical trials of perioperative therapies and for additional surveillance.

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Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients

Abstract: We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

Cited by 42 publications

References 39 publications

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Combining multimodal imaging and treatment features improves machine learning‐based prognostic assessment in patients with glioblastoma multiforme

Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

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