Table 1.
Examples of beneficial information from analyzing tumor heterogeneity
| Indication | Beneficial information from assessment of spatial heterogeneity | |
|---|---|---|
| Examples of current clinical use | Examples of research application | |
| Screening | Subjective morphology (mammography): Lesion spiculation used in BI-RADS in breast cancer (22) | Texture analysis: (mammography): improves sensitivity in distinguishing benign and malignant lesions (31) |
| Diagnosis | Morphology (CT attenuation): Lung nodule speculation (20) | - |
| Staging (TNM) | Hot spot analysis (SUVmax): 18F-FDG PET improves N and M staging in multiple cancers (25) | - |
| Grading | Hot spot analysis (rCBV): DSC-MRI maps targeted biopsy to accurately grade glioma (27) | Histogram analysis (rCBV): improves sensitivity and specificity of grading HGG (38) |
| Hot spot analysis (SUVmax): 18F-FDG PET and 11C-Methionine maps targeted biopsy to accurately grade glioma (26) | ||
| Early Change | - | Histogram analysis (rCBV): detects early transformation of low grade glioma to HGG (40) |
| Partitioning data (ETV and EF): assessment of response, reveals subtle changes in tumor biology (48, 72) | ||
| A priori segmentation (Ktrans): demonstrates differential response in tumor rim and core (62) | ||
| Data-driven segmentation (Ktrans): demonstrates differential response in viable and necrotic tumor regions (79) | ||
| Outcome | - | Histogram analysis (ADC): data relates to OS (42) |
| Feature analysis (CT and MRI): data relates to PFS and OS in various tumor types (55-59) | ||
| Partitioning data (ETV and EF): baseline values prognostic of outcome in HGG (67-69) and cervical cancer (70, 71) | ||
| Partitioning data (SUVmax): persistent values above a threshold indicate poor PFS in GIST or renal cancer treated with TKI (73-75) | ||