Research ArticleCancer Imaging

Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy

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Science Translational Medicine  14 Oct 2015:
Vol. 7, Issue 309, pp. 309ra163
DOI: 10.1126/scitranslmed.aab0195
  • Fig. 1. SRS microscopy workflow and imaging of normal gray and white matter.

    All imaged specimens were collected from patients undergoing anterior temporal lobectomy for intractable epilepsy. (A) Experimental setup of SRS microscopy. The Stokes beam was modulated at high frequency (10 MHz), and the weak stimulated Raman loss signal was demodulated by a lock-in amplifier. A transmission mode detection scheme was used for ex vivo imaging on fresh tissues. DC, dichroic mirror; EOM, electro-optical modulator; FL, optical filter; GM, galvanometer mirror. (B) Raman spectra from fresh sections of human glioblastoma biopsy show white matter, cortex, and tumor. The marked frequencies (dashed lines) at 2845 and 2930 cm−1 were chosen for two-color SRS imaging. au, arbitrary unit. (C) SRS imaging of normal gray matter at high magnification showing neuronal soma with pyramidal architecture filled with lipofuscin-rich granules (left) that stain positively for the neuronal nuclei antigen within the neuronal cell body (right). (D) SRS imaging of white matter (left) demonstrates individual axons appearing as linear, lipid-rich structures that correspond well with neurofilament immunohistochemical staining (right). (E) An SRS image of the gray-white junction (left) demonstrates parallel bundles of lipid-rich white matter tracts that are not visible with hematoxylin and eosin (H&E) staining (right). (F) Capillaries filled with protein-rich erythrocytes appear blue on SRS imaging (left) and eosinophilic on H&E-stained section (right). (G) At low magnification, the biochemical differences between protein-rich gray matter (blue) and myelinated white matter (green) are apparent.

  • Fig. 2. SRS and traditional microscopy of intrinsic brain tumors.

    (A) SRS imaging of a glioblastoma multiforme (GBM) (arrowhead) demonstrating ring enhancement on MRI. (B) Hypercellularity and nuclear atypia of viable tumor are apparent on both SRS (left) and H&E (right) microscopy. (C) Microvascular proliferation creates tortuous vascular complexes evident on SRS microscopy (left, arrowheads) and highlighted with periodic acid–Schiff staining (right, arrowhead). (D) Mitotic figures are also visible (arrowheads) with SRS microscopy (left) and H&E staining (right). (E and F) A non-enhancing, low-grade oligodendroglioma (arrowhead) (E) consists of hypercellular tissue with nests of “fried egg” morphology (arrowheads) (F) causing minimal axonal disruption on SRS imaging (left), as confirmed through neurofilament immunostaining (right). (G and H) “Chicken wire” blood vessels (arrowheads) imaged with SRS (left) and H&E (right) microscopy (G), and perineuronal satellitosis is visible in both SRS (left) and H&E (right) microscopy (H).

  • Fig. 3. SRS microscopy of tissue at the periphery of high- and low-grade gliomas.

    (A) SRS images of the margin of an infiltrating glioblastoma within cortex depicting a transition from densely tumor-infiltrated brain to minimally infiltrated brain (left to right). (B to D) Cellularity and protein/lipid ratio vary in high-magnification images acquired in densely infiltrated tissue (B), moderately infiltrated tissue (C), and minimally infiltrated tissue (D). (E) SRS imaging of an oligodendroglioma infiltrating within white matter, depicting a transition from densely tumor-infiltrated brain to minimally infiltrated brain (left to right). (F to H) Variation in axonal density, cellularity, and protein/lipid ratio is apparent when comparing high-magnification images from densely infiltrated tissue (F), moderately infiltrated tissue (G), and minimally infiltrated tissue (H).

  • Fig. 4. Quantitative analysis of an infiltrative tumor margin imaged with SRS microscopy.

    (A) Cellularity was quantified manually and with automated methods in 20 representative fields of view, drawn from six patients with varying degrees of tumor infiltration (two controls without tumor infiltration, two with infiltrating tumor, and two with dense tumor infiltration). Data are averages ± SEM. (B) The variability in cellularity, axonal density, protein/lipid raio, and classifier values at a brain tumor margin. SRS microscopy lipid and protein channels were overlaid. Heat maps show calculated axon densities (arbitrary units) for all FOVs, nuclei per FOV, calculated protein/lipid ratio for all FOVs, and classifier values for all FOVs. Insets are FOVs with high (red), average (yellow), and low (blue) classifier values.

  • Fig. 5. Nuclear density, axonal density, and protein/lipid ratio are quantified from SRS images.

    (A) Measurements were taken from 1477 300 × 300–μm2 FOVs from 51 fresh tissue biopsies from 18 patients (3 epilepsy patients and 15 patients with brain and spine tumors encompassing eight distinct histologic subtypes). Each point on the scatterplot represents the average value of each biopsy. Biopsies were classified as predominantly normal to minimally hypercellular (n = 21), infiltrating tumor (n = 14), or high-density tumor (n = 16) by a board-certified neuropathologist based on H&E staining. Marker color indicates the mean classifier value for each biopsy, with 0 (most likely normal) depicted in cyan and 1 (most likely tumor) depicted in red. Representative FOVs from normal cortex, normal white matter, low-grade glioma, and high-grade glioma are shown. (B and C) Relationship of classifier values with tumor density (B) and histologic subtype (C). All parameters are normalized to the maximum measurement obtained of that variable and displayed in arbitrary units. Data are means ± SEM.

  • Fig. 6. SRS microscopy within and surrounding a glioblastoma.

    (A) A coronal slice of cadaveric brain from a patient who expired with glioblastoma was sampled at the points indicated in green, shown along 5-mm iso-distance lines (as measured from the tumor margin). (B) FOVs captured from the gross tumor margin (0 mm), 5 mm outside the tumor margin (center), and 15 mm outside the tumor margin reveal dense tumor, infiltrating tumor, and normal tissue by SRS, H&E staining, epidermal growth factor receptor (EGFR) immunohistochemistry, and neurofilament immunostaining. Scale bars, 50 μm. (C) Tukey boxplots showing quantified axonal density, nuclear density, protein/lipid ratio, and classifier values for all FOVs taken from the necrotic tumor core, viable tumor edge, and at 5-mm increments from 5 to 30 mm from the gross tumor margin (n = 8). Outlier cutoff defined as median ± 1.5 interquartile range.

  • Table 1. Quantitative comparison of H&E histology and SRS microscopy.

    Three neuropathologists (R1, R2, and R3) reviewed a series of 75 H&E-stained tissues and 75 matched SRS FOVs and rated the degree of tumor infiltration via a Web-based survey. The category indicated as “normal” in the table represents FOVs categorized as normal to minimally hypercellular tissue with scattered atypical cells.

    DiagnosisModalityNeuropathologist R1Neuropathologist R2Neuropathologist R3Overall
    accuracy (%)
    CorrectIncorrectCorrectIncorrectCorrectIncorrect
    NormalH&E25025024198.7
    SRS24125025098.7
    Infiltrating gliomaH&E141123225082.7
    SRS25018724189.3
    High-density gliomaH&E22325025096.0
    SRS25025023297.3
    TotalsH&E611473274192.4
    SRS74168772395.1
    Both135151419146493.8
  • Table 2. Evaluation of classifiers as indicators of tumor infiltration.

    Nuclear density, axonal density, and protein/lipid ratio were measured for each of the 1477 300 × 300–μm2 FOVs from 51 fresh tissue biopsies from 18 patients. A quasi-likelihood approach with a GAM was used to incorporate all of the attributes into a single classifier. Half of the FOVs (n = 738) were used to create the classifier, which was then tested on the other half of the data (n = 739). Given that glioma can be more difficult to distinguish from normal tissue than metastases and extra-axial tumors, a quasi-likelihood GAM was also used on a subset of tumors, excluding all nonglial tumors, to create the glioma-only classifier. To eliminate correlation between the testing set and training set, we used a leave-one-out cross-validation approach. The leave-one-out cross-validation was performed in a data set excluding nonglioma patients. CI, confidence interval; AUC, area under curve; n/a, not applicable.

    Classification conditionAUCMean sensitivity (%)95% CIMean specificity (%)95% CI
    GAM (all subjects)
      Normal versus abnormal0.99597.595.9–98.998.597.0–99.7
      Normal versus infiltrating0.98894.791.4–98.998.597.0–99.5
      Normal versus dense0.98998.095.6–10099.097.4–100
    GAM (glioma only)
      Normal versus abnormal0.99497.095.0–98.798.797.2–99.5
      Normal versus infiltrating0.98894.991.3–98.198.597.1–99.5
      Normal versus dense0.99098.295.1–10099.098.2–100
    Leave-one-out cross-validation
      Normal versus abnormal0.89387.3n/a87.5n/a
      Normal versus infiltrating0.91182.8n/a95n/a
      Normal versus dense0.90883.9n/a93.3n/a

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/309/309ra163/DC1

    Methods

    Fig. S1. SRS microscopy of pediatric medulloblastoma.

    Fig. S2. SRS microscopy findings in a previously irradiated recurrent oligodendroglioma.

    Fig. S3. SRS microscopy of minimally hypercellular gliomas.

    Fig. S4. SRS and traditional microscopy of extrinsic brain tumors.

    Fig. S5. SRS microscopy of spinal schwannoma.

    Fig. S6. Validation of SRS image segmentation.

    Fig. S7. Quantitative analysis of a normal specimen imaged with SRS microscopy.

    Fig. S8. Quantification FOVs used to create the classifier.

    Fig. S9. Planned workflow for ex vivo SRS-guided brain tumor resection.

    Fig. S10. Planned workflow for in vivo SRS-guided brain tumor resection.

    Table S1. Descriptive statistics of the test case series.

    Table S2. In-depth verification of automated method for cellular density quantification.

    Table S3. Test characteristics of independent biopsy parameters and the classifier as predictors of the presence of tumor infiltration.

    Table S4. Comparison of pathologist and classifier performance on SRS microscopy survey.

    Reference (46)

  • Supplementary Material for:

    Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy

    Minbiao Ji, Spencer Lewis, Sandra Camelo-Piragua, Shakti H. Ramkissoon, Matija Snuderl, Sriram Venneti, Amanda Fisher-Hubbard, Mia Garrard, Dan Fu, Anthony C. Wang, Jason A. Heth, Cormac O. Maher, Nader Sanai, Timothy D. Johnson, Christian W. Freudiger, Oren Sagher, Xiaoliang Sunney Xie*, Daniel A. Orringer*

    *Corresponding author. E-mail: dorringe{at}med.umich.edu (D.A.O.); xie{at}chemistry.harvard.edu (X.S.X.)

    Published 14 October 2015, Sci. Transl. Med. 7, 309ra163 (2015)
    DOI: 10.1126/scitranslmed.aab0195

    This PDF file includes:

    • Methods
    • Fig. S1. SRS microscopy of pediatric medulloblastoma.
    • Fig. S2. SRS microscopy findings in a previously irradiated recurrent oligodendroglioma.
    • Fig. S3. SRS microscopy of minimally hypercellular gliomas.
    • Fig. S4. SRS and traditional microscopy of extrinsic brain tumors.
    • Fig. S5. SRS microscopy of spinal schwannoma.
    • Fig. S6. Validation of SRS image segmentation.
    • Fig. S7. Quantitative analysis of a normal specimen imaged with SRS microscopy.
    • Fig. S8. Quantification FOVs used to create the classifier.
    • Fig. S9. Planned workflow for ex vivo SRS-guided brain tumor resection.
    • Fig. S10. Planned workflow for in vivo SRS-guided brain tumor resection.
    • Table S1. Descriptive statistics of the test case series.
    • Table S2. In-depth verification of automated method for cellular density quantification.
    • Table S3. Test characteristics of independent biopsy parameters and the classifier as predictors of the presence of tumor infiltration.
    • Table S4. Comparison of pathologist and classifier performance on SRS microscopy survey.
    • Reference (46)

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