Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning

BACKGROUND AND PURPOSE

MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data.

MATERIALS AND METHODS

We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models.

RESULTS

Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%).

CONCLUSIONS

Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma

BACKGROUND AND PURPOSE

Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS

This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.

RESULTS

With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS

Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma

BACKGROUND AND PURPOSE

Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG.

MATERIALS AND METHODS

We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-μm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression.

RESULTS

We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17).

CONCLUSIONS

rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.

Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas

BACKGROUND AND PURPOSE

Relative cerebral blood volume (rCBV) accuracy can vary substantially depending on the dynamic susceptibility-weighted contrast-enhanced (DSC) acquisition and postprocessing methods, due to blood-brain barrier disruption and resulting T1-weighted leakage and T2- and/or T2*-weighted imaging (T2/T2*WI) residual effects. We set out to determine optimal DSC conditions that address these errors and maximize rCBV accuracy in differentiating posttreatment radiation effect (PTRE) and tumor.

MATERIALS AND METHODS

We recruited patients with previously treated high-grade gliomas undergoing image-guided re-resection of recurrent contrast-enhancing MR imaging lesions. Thirty-six surgical tissue samples were collected from 11 subjects. Preoperative 3T DSC used 6 sequential evenly timed acquisitions, each by using a 0.05-mmol/kg gadodiamide bolus. Preload dosing (PLD) and baseline subtraction (BLS) techniques corrected T1-weighted leakage and T2/T2*WI residual effects, respectively. PLD amount and incubation time increased with each sequential acquisition. Corresponding tissue specimen stereotactic locations were coregistered to DSC to measure localized rCBV under varying PLD amounts, incubation times, and the presence of BLS. rCBV thresholds were determined to maximize test accuracy (average of sensitivity and specificity) in distinguishing tumor (n = 21) and PTRE (n = 15) samples under the varying conditions. Receiver operator characteristic (ROC) areas under the curve (AUCs) were statistically compared.

RESULTS

The protocol that combined PLD (0.1-mmol/kg amount, 6-minute incubation time) and BLS correction methods maximized test AUC (0.99) and accuracy (95.2%) compared with uncorrected rCBV AUC (0.85) and accuracy (81.0%) measured without PLD and BLS (P = .01).

CONCLUSIONS

Combining PLD and BLS correction methods for T1-weighted and T2/T2*WI errors, respectively, enables highly accurate differentiation of PTRE and tumor growth.

Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements

BACKGROUND AND PURPOSE

Differentiating tumor growth from posttreatment radiation effect (PTRE) remains a common problem in neuro-oncology practice. To our knowledge, useful threshold relative cerebral blood volume (rCBV) values that accurately distinguish the 2 entities do not exist. Our prospective study uses image-guided neuronavigation during surgical resection of MR imaging lesions to correlate directly specimen histopathology with localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC) measurements and to establish accurate rCBV threshold values, which differentiate PTRE from tumor recurrence.

MATERIALS AND METHODS

Preoperative 3T gradient-echo DSC and contrast-enhanced stereotactic T1-weighted images were obtained in patients with high-grade glioma (HGG) previously treated with multimodality therapy. Intraoperative neuronavigation documented the stereotactic location of multiple tissue specimens taken randomly from the periphery of enhancing MR imaging lesions. Coregistration of DSC and stereotactic images enabled calculation of localized rCBV within the previously recorded specimen locations. All tissue specimens were histopathologically categorized as tumor or PTRE and were correlated with corresponding rCBV values. All rCBV values were T1-weighted leakage-corrected with preload contrast-bolus administration and T2/T2*-weighted leakage-corrected with baseline subtraction integration.

RESULTS

Forty tissue specimens were collected from 13 subjects. The PTRE group (n = 16) rCBV values ranged from 0.21 to 0.71, tumor (n = 24) values ranged from 0.55 to 4.64, and 8.3% of tumor rCBV values fell within the PTRE group range. A threshold value of 0.71 optimized differentiation of the histopathologic groups with a sensitivity of 91.7% and a specificity of 100%.

CONCLUSIONS

rCBV measurements obtained by using DSC and the protocol we have described can differentiate HGG recurrence from PTRE with a high degree of accuracy.

Treatment of Recurrent Previously Coiled Anterior Circulation Aneurysm with Minimally Invasive Keyhole Craniotomy: Report of Two Cases

The use of minimally invasive techniques has not yet been reported for the treatment of recurrent aneurysms after coil embolization. A 47-year-old man with a long history of headaches had an anterior communicating aneurysm that had previously been coil embolized. Three-year follow-up angiography showed a significant recurrence. A 50-year-old woman with subarachnoid hemorrhage and acute visual loss underwent coil embolization of a large ophthalmic artery aneurysm, which recurred 3 months later. In both cases, a keyhole fronto-orbital one-piece craniotomy was performed. In the first patient, the aneurysm was clip ligated. The coil mass, which had eroded through the dome, was excised. In the second patient, the anterior clinoid was removed and the aneurysm was clip ligated. Postoperative angiography showed no residual aneurysm and no evidence of branch or parent vessel compromise in either patient. Both patients had an uncomplicated postoperative course. Recurrent previously coiled aneurysms are technically challenging to treat. A minimal fronto-orbital craniotomy provides a sufficiently capacious working space for successful treatment of some recurrent aneurysms of the anterior circulation.

Resolution of Trigeminal Neuralgia Following Third Ventriculostomy for Hydrocephalus Associated with Chiari I Malformation: Case Report

OBJECTIVE AND IMPORTANCE

Cranial nerve dysfunction, including trigeminal neuralgia, has been associated with Chiari I malformations. In such cases, trigeminal neuralgia is thought to be related to tonsillar compression of the brainstem or to traction on the cranial nerves. Hydrocephalus may be a contributing factor.

CLINICAL PRESENTATION

A 38-year-old woman had right-sided lancinating facial pain typical of trigeminal neuralgia but was otherwise neurologically intact. Magnetic resonance imaging showed no evidence of a compressing vessel. Moderate hydrocephalus and a Chiari I malformation were noted incidentally. The visibility of the aqueduct was poor.

INTERVENTION

The patient underwent a third ventriculostomy and her symptoms resolved completely.

CONCLUSION

This is the first case in which trigeminal neuralgia was treated with a third ventriculostomy and one of only four cases of isolated trigeminal neuralgia associated with a Chiari malformation. Acquired aqueductal stenosis may have caused the hydrocephalus which, in turn, caused the Chiari malformation configuration that caused the trigeminal neuralgia. The rationale for the treatment modality and possible causes of Chiari I-induced trigeminal neuralgia are discussed.

Traumatic brain injury: patterns of failure of nonoperative management

OBJECTIVE

The circumstances of failure for nonoperative management of blunt traumatic brain injury have been poorly defined. In this study, all trauma patients identified over a 12-year period with progression of neurologic injury requiring craniotomy were retrospectively reviewed.

METHODS

Data collected included demographic information, mechanism of injury, field and admission vital signs, and Glasgow Coma Scale score, medications, associated injuries, and coagulopathy. Head computed tomographic scans were reviewed, and anatomic findings were correlated with clinical changes (change in mental status or elevation of intracranial pressure) that led to subsequent CT scan and craniotomy.

RESULTS

Of 20,100 patients, there were 852 who had computed tomographic scans with acute intracranial injuries on admission; 462 patients were managed nonoperatively. Fifty-seven patients had progression of neurologic injury (34 < 24 hours = early; 23 > 24 hours = late) that required surgery.

CONCLUSION

Of the variables investigated, only anatomic location of injury was found to be predictive of early failure of nonoperative management. Frontal intraparenchymal hematomas are particularly prone to early failure. Clinical examination and intracranial pressure monitoring are equally important in detecting failure and should be an integral part of nonoperative management.

Arteriovenous malformations of the posterior fossa: a report on 28 cases and review of the literature

Posterior fossa arteriovenous malformations are uncommon lesions accounting for between 7 and 18% of all intracranial arteriovenous malformations. They have a greater incidence of haemorrhage and a higher morbidity and mortality if untreated compared with those localized in the supratentorial compartment. We report our experience with 28 cases of posterior fossa arteriovenous malformations referred to the senior author between January 1971 and December 1993. The anatomy, symptomatology, treatment and results are discussed with regard to the most recent literature.