Key points
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Biomarkers derived from medical images inform on disease detection, characterisation and treatment response.
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Quantitative imaging biomarkers have potential to provide objective decision-support tools in the management pathway of patients.
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Measurement variability needs to be understood and systems for data acquisition and analysis harmonised before using quantitative imaging measurements to drive clinical decisions.
Introduction
Validated imaging biomarkers currently used to support clinical decision-making
Semi-quantitative scoring systems
Quantitative measures of size/volume
Extractable quantitative imaging biomarkers with potential to support clinical decision-making
Ultrasound
CT
MR including multiparametric data
Positron emission tomography (PET)-SUV metrics
Disease detection | |||||||
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Biomarker | SemiQ/Q | Disease | Question answered | Utility of biomarker | Data from | Potential decision for | |
Non-malignant disease |
LVEF-US
LVEF-MRI
| Q | Cardiac function | Cardiac output Cardiac output | ICC US 0.72, single centre sensitivity 69% [29] ICC MRI 0.86,correlation of MRI and cineventriculography 0.72 [99] | Single centre US | Inotropes Inotropes |
Renal volume-US, CT, MRI | Q | Renal failure | Mass of parenchyma | ICC on US 0.64–0.86 [101] Correlation of US with CT 0.76–0.8 [102] Interobserver reproducibility on MRI 87–88% [103] | Single centre | Renal replacement, safety and toxicity of other pharmaceuticals | |
Young’s modulus on elastography-US | Q | Parkinson’s disease | Tumour presence Muscle stiffness | Thyroid sensitivity 80%, specificity 95% [104] Breast AUC 0.898 for conventional US, 0.932 for shear wave elastography, and 0.982 for combined data [105] Prostate sensitivity 0.84, spec 0.84 [51] | Thyroid, breast: single centre Prostate meta-analysis | Treatment with surgery/radiotherapy/chemotherapy | |
Lung tissue density | Q | Airways obstruction, interstitial lung disease present | Emphysema (density assessment) influences BODE (body mass index, airflow obstruction, dyspnea and exercise capacity) index. Odds ratio of interstitial lung abnormalities for reduced lung capacity 2.3 | Multicentre Single centre | Surgery, valve and drug treatment | ||
Fibrosis and ground-glass index on CT lung | SQ | Idiopathic lung fibrosis | Development of inflammation and fibrosis | Mortality predicted by pulmonary vascular volume (HR 1.23 (1.08–1.40), p = 0.001) and honeycombing (HR 1.18 (1.06–1.32), p = 0.002) [108] | Single centre | Drug treatment | |
ADC/pCT | SQ | Ischaemic stroke | Presence of salvageable tissue versus infarct core | Measure of infarct core/penumbra used for patient stratification for research [109] | Planned multicentre | Treatment | |
Malignant disease | SQ | Lung nodules | Risk of malignancy | AUC for malignancy 0.81–0.87 [110] | Multicentre | Time period of follow-up or surgery | |
CT blood flow, perfusion, permeability metrics | Q | Malignant neck lymph nodes Hepatocellular cancer | Tumour presence | Sensitivity 0.73, specificity 0.70 [112] AUC 0.75, sensitivity 0.79, specificity 0.75 [113] | Single centre Single centre | Staging and management (surgery, radiotherapy or chemotherapy) | |
BI-RADS [114] PI-RADS [115] LI-RADS [116] | SQ | Cancer | Risk of malignancy | PPV: BI-RADS0 14.1 %, BI-RADS4 39.1 % and BI-RADS5 92.9 % PI-RADS2 pooled sensitivity 0.85, pooled specificity 0.71 Pooled sensitivity for malignancy 0.93 | Dutch breast cancer screening programme Meta-analysis Systematic review | Staging and management stratification (surgery, radiotherapy, chemotherapy, combination) | |
ADC
| Q | Cancer [117] Liver lesions [118] Prostate cancer [119] | Tumour presence | Liver AUC 0.82–0.95 Prostate AUC 0.84 | Single centre Single centre | Staging and management stratification (surgery, radiotherapy, chemotherapy, combination) | |
Dynamic contrast enhanced metrics (Ktrans, Kep, blood flow, Ve) | Q | Liver tumour Recurrent glioblastoma | Hepatocellular cancer AUC 0.85, sensitivity 0.85, specificity 0.81 [113] Brain- KtransAccuracy 86% [120] | Single centre Single centre | Further treatment | ||
18
FDG SUV
| Q | Cancer Sarcoma [121] Lung cancer [105] | Tumour presence | Sarcoma—sensitivity 0.91, specificity 0.85, accuracy 0.88 Lung—sensitivity 0.68 to 0.95 depending on histology | Meta-analysis Meta-analysis | Staging and management stratification (surgery, radiotherapy, chemotherapy, combination) | |
Targeted radionuclides | Non-Q | Cancer | Tumour presence | Sensitivity 97% and specificity 92% for octreotide [126] Sensitivity 100% and specificity 100% for PSMA [127] | Single centre Single centre | Validation remains difficult because of biopsying multiple positive sites. |
Radiomic signature biomarkers
Selecting and translating appropriate imaging biomarkers to support clinical decision-making
Biomarker | SemiQ/Q | Disease | Question answered | Utility of biomarker | Data from | Potential decision for | |
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Non-malignant disease | Young’s modulus | Q | Coronary plaques [53] | Risk of rupture | Reproducibility CoV 22% vessel wall, 19% in plaque. AUC for focal neurology Youngs modulus + degree = 0.78 | Single centre | Stenting, coronary bypass surgery |
Plaque density, vessel luminal diameter | Q | Coronary artery stenosis | Risk of plaque rupture; risk of significant cardiac ischaemia, infarction, death | No luminal narrowing but with coronary artery calcium (CAC) score > 0 had a 5-year mortality HR 1.8 compared with those whose CACS = 0. No luminal narrowing but CAC ≥ 100 had mortality risks similar to individuals with non-obstructive coronary artery disease [138] CT angiography significantly better at predicting events than stress echo/ECG [68] Coronary death/non-fatal myocardial infarction was lower in patients with stable angina receiving CT angiography than in the standard-care group (HR = 0.59) [69] | Multicentre Multicentre Multicentre | Statins, stenting, coronary bypass surgery | |
18F-Na | SQ | Aortic valve disease Coronary plaque [139] Acute events from abdominal aortic aneurysm | Valve stenosis present Likelihood of plaque rupture Likelihood of aneurysm rupture | Reproducibility NaF uptake 10% [140] Baseline 18F-NaF uptake correlated closely with the change in calcium score at 1 year [141] 18F-NaF uptake (maximum tissue-to-background ratio 1·90 [IQR 1.61–2.17]) associated with ruptured plaques and those with high-risk features [142] Aneurysms in the highest tertile of 18F-NaF uptake expanded 2.5× more rapidly than those in the lowest tertile and were 3× more likely to rupture [143] | Single Multicentre | Coronary stenting, aneurysm stenting | |
MTR | Q | Multiple sclerosis | Disease progression | MTR significantly correlates with T2 lesion volume [144] Grey matter MTR histogram peak height and average lesion MTR percentage change after 12 months independent predictors of disability worsening at 8 years [145] Change in brain MTR specificity 76.9% and PPV 59.1% for Expanded Disability Status Scale score deterioration [146] | Multicentre Single centre Single centre | Timing of therapeutic intervention | |
Malignant disease |
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FDG-SUV
| Q | Cancer Oesophageal cancer | Good or poor prognosis tumour in terms of PFS and OS | Wide variation between individuals and tumours [147] Oesophageal cancer HR 1.86 for OS, 2.52 for DFS [148] | Meta-analysis | Neoadjuvant or adjuvant therapy or treatment modality combinations |
18FLT-SUV | Q | Cancer | High proliferative activity present | Sizeable overlap in values with normal proliferating tissues [75] | Review of data from single centre studies | Neoadjuvant or adjuvant therapy or treatment modality combinations | |
ADC
MRF (ADC, T1 and T2) | Q Q Q | Cancer, correlates with tumour grade | Risk of recurrence or metastasis | Area under ROC, sensitivity and specificity of nADCmean for G3 intrahepatic cholangiocarcinoma versus G1+G2 were 0.71, 89.5% and 55.5% [149] “Unfavourable” ADC in cervix cancer predictive of disease-free survival (HR 1.55) [150] ADC and T2 together give AUC of 0.83 for separating high- or intermediate-grade from low-grade prostate cancer [151] | Single centre Meta-analysis Single centre | Need of biopsy or other invasive diagnosis Neoadjuvant or adjuvant therapy Decision for radical treatment or active surveillance | |
DSC-MRI | SQ (rCBV) | Brain cancer | Grading glioma | AUC = 0.77 for discriminating glioma grades II and III [152] | Meta-analysis | Type and time of intervention/treatment | |
APT | Q | Glioma | Proliferation | APT correlates with tumour grade and Ki67 index [153] | Single centre | Therapeutic strategies | |
DCE-CT parameters Blood flow, permeability | Q | Rectal cancer Lung cancer | Blood flow 75% accuracy for detecting rectal tumours with lymph node metastases [154] CT permeability predicted survival independent of treatment in lung cancer [155] | Single centre Single centre | Surgical dissection, adjuvant radiotherapy Adjuvant therapy | ||
DCE-MRI parameters | Q | Cervix cancer Endometrial cancer Rectal cancer Breast cancer | Risk of recurrence or metastasis, survival | Tumour volume with increasing signal is a strong independent prognostic factor for DFS and OS in cervical cancer [156] Low tumour blood flow and low rate constant for contrast agent intravasation (kep) associated with high-risk histological subtype in endometrial cancer [157] Ktrans, Kep and Ve significantly higher in rectal cancers with distant metastasis [158] Ktrans, iAUCqualitative and ADC predict low-risk breast tumors (AUC of combined parameters 0.78) | Single centre Single centre Single centre Single centre | Neoadjuvant, adjuvant or multimodality treatment strategies | |
Radiomic signature [159] | Q | Tumour with good or poor prognosis | Data endpoints, feature selection techniques and classifiers were significant factors in affecting predictive accuracy in lung cancer [162] Radiomic signature (24 selected features) is significantly associated with LN status in colorectal cancer [163] | Single centre Single centre | Neoadjuvant or adjuvant treatment, immunotherapy Lymph node dissection, adjuvant treatment |
Biomarker | SemiQ/Q | Disease | Question answered | Utility of biomarker | Data from | Potential decision for | |
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Non-malignant disease | Volumetric high resolution CT density (quantitative interstitial lung disease, QILD) | Q | Scleroderma | Response to cyclophosphamide | 24-month changes in QILD scores in the whole lung correlated significantly 24-month changes in forced vital capacity (ρ = − 0.37), diffusing capacity (ρ = − 0.22) and breathlessness (ρ = − 0.26) [164] | Single centre | Continue, change or stop treatment |
Left Ventricular ejection fraction LVEF | Q | Pulmonary hypertension Myocardial ischaemia/infarction | Right and left cardiac sufficiency Improvement in cardiac function | Increases in 6-min walk distance were significant correlated with change in right ventricular ejection fraction and left ventricular end-diastolic volume [165] Monitoring cardiac function [166] | Multicentre Multicentre | Continue, change or stop treatment | |
Malignant disease | RECIST/morphological volume | Q | Cancer | Response | Current guidelines for response assessment [167] | Multicentre | Continue, change or stop treatment |
PERCIST/metabolic volume [168] | Q | Cancer | Response | Current guidelines for response assessment | Multicentre | Continue, change or stop treatment | |
Scoring systems for disease burden | SQ | Multiple sclerosis Rheumatoid arthritis | Reduction in disease burden | Effects on MRI lesions over 6–9 months predict the effects on relapses at 12–24 months) [169] International consensus on scoring system [170] | Meta-analysis Review | Continue, change or stop therapy | |
DSC-MRI | SQ (rCBV) | Brain cancer | Differentiation of treatment effects and tumour progression | Meta-analysis | Decision to treat | ||
Q | Multiple cancer types | Response to therapy | Rectal cancer-pooled sensitivity, 73%; pooled specificity, 77%; pooled AUC, 0.83 [174] Intratreatment low SUVmax (persistent low or decrease of 18F-FDG uptake) predictive of loco-regional control in head and neck cancer [175] | Meta-analysis Meta-analysis | Continue, change or stop therapy | ||
Deauville or RECIL score on
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F-FDG-PET
| SQ | Lymphoma | CR, PR, SD or PD [176] | Assessment of tumour burden in lymphoma clinical trials can use the sum of longest diameters of a maximum of three target lesions [177] | Multicentre | Continue, change or stop therapy | |
Targeted agents HER2 PSMA | SQ | Breast cancer [178] Prostate cancer [179] | Reduction in tumour cells expressing these antigens | Tumour receptor specific Effects of treatment on receptor expression | Single centre studies, review | Continue, change or stop therapy | |
ADC [117] | SQ Q | Rectal cancer Breast cancer | Response to neoadjuvant chemotherapy Response to neoadjuvant chemotherapy | Additional value in both the prediction and detection of (complete) response to therapy compared with conventional sequences alone [180] After 12 weeks of therapy, change in ADC predicts complete pathologic response to neoadjuvant chemotherapy (AUC = 0.61, p = 0.013) [181] | Review Multicentre | Continue, change or stop therapy, proceed to surgery | |
CT perfusion/blood flow | Q | Oesophageal cancer | Response to chemoradiotherapy | Multivariate analysis identified blood flow as a significant independent predictor of response [182] | Single centre | Further treatment | |
DCE-MR parameters | Q | Multiple cancer types | Response to therapy | Particular benefit in assessing therapy response to antiangiogenic agents [183] | Review | Change therapeutic strategy | |
CT density HU | Q | Gastrointestinal stromal tumours | Response to chemotherapy | Decrease in tumour density of > 15% on CT had a sensitivity of 97% and a specificity of 100% in identifying PET responders versus 52% and 100% by RECIST [184] | Continue, change or stop therapy |
Advancing new quantitative imaging biomarkers as decision-support tools to clinical practice
Recommendation | Current evidence | Action needed |
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Consider need for quantitation in relation to the decision being made | Semi-quantitative imaging biomarkers are successfully used in many clinical pathways. | • Classification systems retain a subjective element that could benefit from standardisation and refinement. • Development of automated and thresholding would enable more quantitative assessments |
Use validated IB methodology for semi-quantitative and quantitative measures | Many single and multicentre trials validating quantitative imaging biomarkers with clinical outcome now exist. | • Harmonisation of methodology • Standardised reporting systems |
Establish evidence on the use of quantitation by inclusion into clinical trials | Clinical trials are usually planned by non-imagers. Integration of imaging biomarkers into trials is dependent on what is available routinely to non-imagers in the clinic, rather than exploiting an imaging technique to its optimal potential. | • Inventory of imaging biomarkers accessible through a web-based portal would inform the inclusion and utilisation of imaging biomarkers within trials (The European Imaging Biomarkers Alliance initiative). • Certified biomarkers conforming to set standards (Quantitative Imaging Biomarkers Alliance initiative) |
Validate against pathology or clinical outcomes to make imaging a “virtual biopsy” | Several major databanks hold imaging and clinical or pathology data • CaBIG (USA) • UK MRC Biobank (UK) • German National Cohort Study (Germany) | • Large data collection for validation of imaging and pathology • Curation in imaging biobanks |
Select appropriate quality assured quantitative IB | Trials with embedded QA/QC procedures have indicated good reproducibility of quantitative imaging biomarkers (e.g. EU iMi QuIC:ConCePT project) | • Ensure curation and archiving of longitudinal imaging data with outcomes within trials |
Open-source interchange kernel | Low comparability between image-derived biomarkers if hardware and software of different manufacturers are used. | • Harmonisation of image acquisition and post-processing over manufacturers |