Standard-dose vs. low-dose CT protocols in the evaluation of localized lung lesions: Capability for lesion characterization-iLEAD study

Accuracy of patient-specific CT organ doses from Monte Carlo simulations: influence of CT-based voxel models

Monte Carlo simulations using patient CT images as input are the gold standard to perform patient-specific dosimetry. However, in standard clinical practice patient's CT images are limited to the reconstructed CT scan range. In this study, organ dose calculations were performed with ImpactMC for chest and cardiac CT using whole-body and anatomy-specific voxel models to estimate the accuracy of CT organ doses based on the latter model. When the 3D patient model is limited to the CT scan range, CT organ doses from Monte Carlo simulations are the most accurate for organs entirely in the field of view. For these organs only the radiation dose related to scatter from the rest of the body is not incorporated. For organs lying partially outside the field of view organ doses are overestimated by not accounting for the non-irradiated tissue mass. This overestimation depends strongly on the amount of the organ volume located outside the field of view. To get a more accurate estimation of the radiation dose to these organs, the ICRP reference organ masses and densities could form a solution. Except for the breast, good agreement in dose was found for most organs. Voxel models generated from clinical CT examinations do not include the overscan in the z-direction. The availability of whole-body voxel models allowed to study this influence as well. As expected, overscan induces slightly higher organ doses.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

Cancer risk in healthy patients who underwent chest tomography comparing three different technologies

This research aimed to estimate the risk of cancer associated with patients without previous disease undergoing chest tomography. Siemens CT scanners have 6, 64, and 128 detectors. The Biologic Effects of Ionizing Radiation Reports - BEIR VII methodology was used. The study presented a sample of 64 patients aged between 18 and 80 years, in the city of Belo Horizonte, Minas Gerais - Brazil. The IMPACT CT software and CalDose X CT Online were used to calculate the absorbed and equivalent dose from the Volumetric Computed Tomography Dose Index - CTDIvol (mGy) and Dose Length Product - DLP values provided by the equipment. CT-Expo Software was also used to estimate Specific Dose Estimates (SSDEs) values. The CTDvol results for the MG1, MG,2 and MG3 Diagnostic Centers in mGy were respectively 4.369 ± 1.352, 6.99 4 ± 1.53,3 and 9.984 ± 2.282 and the SSDE values were 3.800, 6.40,0 and 9,.500. The values for the equivalent dose, at the MG2 Diagnostic Center, by IMPACT CT, in (mSv) for the breasts, esophagus, heart, thyroid, lung and thymus were respectively 3.9, 5.7, 4.7, 1.0, 4.8 and 5.7. The CalDose Software, for the same equipment and the same organs, in mSv, estimated the values 7.4, 9.4, 11.1, 5.3, 10.8 and 11.3 for women and 7.1, 9.3, 11.0, 5.3, 10.2 and 10.9 for men. The estimated risk of cancer decreased according to the patient's age, but with a higher incidence for females. The use of each software must be carefully analyzed to avoid undue values due to the particularities of each one. The results also showed that the risk of developing cancer due to radiation decreases with patient age and is higher in females.

Sequential low-dose CT thorax scans to determine invasive pulmonary fungal infection incidence after allogeneic hematopoietic cell transplantation

Invasive fungal disease (IFD) during neutropenia goes along with a high mortality for patients after allogeneic hematopoietic cell transplantation (alloHCT). Low-dose computed tomography (CT) thorax shows good sensitivity for the diagnosis of IFD with low radiation exposure. The aim of our study was to evaluate sequential CT thorax scans at two time points as a new reliable method to detect IFD during neutropenia after alloHCT. We performed a retrospective single-center observational study in 265/354 screened patients admitted for alloHCT from June 2015 to August 2019. All were examined by a low-dose CT thorax scan at admission (CT t₀) and after stable neutrophil recovery (CT t₁) to determine the incidences of IFD. Furthermore, antifungal prophylaxis medications were recorded and cohorts were analyzed for statistical differences in IFD incidence using the sequential CT scans. In addition, IFD cases were classified according to EORTC 2008. At CT t₀ in 9.6% of the patients, an IFD was detected and antifungal therapy initiated. The cumulative incidence of IFD in CT t₁ in our department was 14%. The use of Aspergillus-effective prophylaxis through voriconazole or posaconazole decreased CT thorax t₁ suggesting IFD is statistically significant compared to prophylaxis with fluconazole (5.6% asp-azol group vs 16.3% fluconazole group, p = 0.048). In 86%, CT t₁ was negative for IFD. Low-dose sequential CT thorax scans are a valuable tool to detect pulmonary IFDs and guide antifungal prophylaxis and therapies. Furthermore, a negative CT t₁ scan shows a benefit by allowing discontinuation of antifungal medication sparing patients from drug interactions and side effects.

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.

Nationwide lung cancer screening with low-dose computed tomography: implementation and first results of the HUNCHEST screening program

OBJECTIVES

Lung cancer (LC) kills more people than any other cancer in Hungary. Hence, there is a clear rationale for considering a national screening program. The HUNCHEST pilot program primarily aimed to investigate the feasibility of a population-based LC screening in Hungary, and determine the incidence and LC probability of solitary pulmonary nodules.

METHODS

A total of 1890 participants were assigned to undergo low-dose CT (LDCT) screening, with intervals of 1 year between procedures. Depending on the volume, growth, and volume doubling time (VDT), screenings were defined as negative, indeterminate, or positive. Non-calcified lung nodules with a volume > 500 mm³ and/or a VDT < 400 days were considered positive. LC diagnosis was based on histology.

RESULTS

At baseline, the percentage of negative, indeterminate, and positive tests was 81.2%, 15.1%, and 3.7%, respectively. The frequency of positive and indeterminate LDCT results was significantly higher in current smokers (vs. non-smokers or former smokers; p < 0.0001) and in individuals with COPD (vs. those without COPD, p < 0.001). In the first screening round, 1.2% (n = 23) of the participants had a malignant lesion, whereas altogether 1.5% (n = 29) of the individuals were diagnosed with LC. The overall positive predictive value of the positive tests was 31.6%. Most lung malignancies were diagnosed at an early stage (86.2% of all cases).

CONCLUSIONS

In terms of key characteristics, our prospective cohort study appears consistent to that of comparable studies. Altogether, the results of the HUNCHEST pilot program suggest that LDCT screening may facilitate early diagnosis and thus curative-intent treatment in LC.

KEY POINTS

• The HUNCHEST pilot study is the first nationwide low-dose CT screening program in Hungary. • In the first screening round, 1.2% of the participants had a malignant lesion, whereas altogether 1.5% of the individuals were diagnosed with lung cancer. • The overall positive predictive value of the positive tests in the HUNCHEST screening program was 31.6%.

A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage

PURPOSE

With the future goal of defining a large dataset based on low-dose CT with labelled pulmonary lesions for lung cancer screening (LCS) research, the aim of this work is to propose and evaluate into a clinical context a tool for semi-automatic segmentation able to facilitate the process of labels collection from a LCS study (COSMOS, Continuous Observation of SMOking Subjects).

METHODS

Considering a preliminary set of manual annotations, a segmentation model based on a 2D-Unet was trained from scratch. Contour quality of the final 2D-Unet was assessed on an internal test set of manual annotations and on a subset of the public available LIDC dataset used as external test set. The tool for semi-automatic segmentation was then designed integrating the tested model into a Graphical User Interface. According to the opinion of two clinical users, the percentage of lesions properly contoured through the tool was quantified (Acceptance Rate, AR). The variability between segmentations derived by the two readers was estimated as mean percentage of difference (MPD) between the two sets of volumes and comparing the likelihood of malignancy derived from Volume Doubling Time (VDT).

RESULTS

Performance in test sets were found similar (DICE ~ 0.75(0.15)). Accordingly, a good mean AR (80.1%) resulted from the two readers. Variability in terms of MPD was equal to 23.6% while 2.7% was the VDTs percentage of disagreement.

CONCLUSIONS

A semi-automatic segmentation tool was developed and its applicability evaluated into a clinical context demonstrating the efficacy of the tool in facilitating the collection of labelled data.

The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review

INTRODUCTION

Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT.

METHODS

Literature searches of ProQuest, PubMed, Cinahl, ScienceDirect, EbscoHost Ebook Collection and Ovid were carried out to find research articles published between the years 2015 and 2020. In addition, manual search was conducted in SweMed+, SwePub, NORA, Taylor & Francis Online and Medic.

RESULTS

Following a systematic search process, the review comprised of 16 articles. Articles were organised according to the effects of the deep learning networks, e.g. image noise reduction, image restoration. Deep learning can be used in multiple ways to facilitate dose optimisation in low-dose CT. Most articles discuss image noise reduction in low-dose CT.

CONCLUSION

Deep learning can be used in the optimisation of patients' radiation dose. Nevertheless, the image quality is normally lower in low-dose CT (LDCT) than in regular-dose CT scans because of smaller radiation doses. With the help of deep learning, the image quality can be improved to equate the regular-dose computed tomography image quality.

IMPLICATIONS TO PRACTICE

Lower dose may decrease patients' radiation risk but may affect the image quality of CT scans. Artificial intelligence technologies can be used to improve image quality in low-dose CT scans. Radiologists and radiographers should have proper education and knowledge about the techniques used.

DNA methylation of PTGER4 in peripheral blood plasma helps to distinguish between lung cancer, benign pulmonary nodules and chronic obstructive pulmonary disease patients

BACKGROUND/INTRODUCTION

In contrast to patients who present with advanced stage lung cancer and associated poor prognosis, patients with early-stage lung cancer may be candidates for curative treatments. The results of the NELSON lung cancer screening trial are expected to stimulate the development and implementation of a lung cancer screening strategy in most countries. Widespread use of chest computed tomography scans will also result in the detection of solitary pulmonary nodules. Because reliable biomarkers to distinguish between malignant and benign lesions are lacking, tissue-based histopathological diagnostics remain the gold standard. In this study, we aimed to establish a test to assess the predictive ability of DNA hypermethylation of SHOX2 and PTGER4 in plasma to discriminate between patients with 1.) lung cancer, 2.) benign lesions, and 3.) patients with chronic obstructive pulmonary disease (COPD).

PATIENTS AND METHODS

We retrospectively analysed SHOX2 and PTGER4 methylation in 121 prospectively collected plasma samples of patients with lung cancer (group 1A), benign lesions (group 1B), and COPD without nodules (group 2).

RESULTS

PTGER4 DNA hypermethylation was more frequently observed in patients with lung cancer than in controls (p = 0.0004). Results remained significant after correction for tumour volume, smoking status, age, and eligibility for the NELSON trial.

CONCLUSIONS

Detection of methylated PTGER4 in plasma DNA may serve as a biomarker to support clinical decision-making in patients with pulmonary lesions at lung cancer screening in high-risk populations. Further exploration in prospective studies is warranted.

Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review

PURPOSE

Studies have evaluated imaging modalities with a lower radiation dose than standard-dose CT (SD-CT) for chest examination. This systematic review aimed to summarize evidence on diagnostic accuracy of these modalities - low-dose and ultra-low-dose CT (LD- and ULD-CT) - for chest pathology.

METHOD

Ovid-MEDLINE, Ovid-EMBASE and the Cochrane Library were systematically searched April 29th-30th, 2019 and screened by two reviewers. Studies on diagnostic accuracy were included if they defined their index tests as 'LD-CT', 'Reduced-dose CT' or 'ULD-CT' and had SD-CT as reference standard. Risk of bias was evaluated on study level using the Quality Assessment of Diagnostic Accuracy Studies-2. A narrative synthesis was conducted to compare the diagnostic accuracy measurements.

RESULTS

Of the 4257 studies identified, 18 were eligible for inclusion. SD-CT (3.17 ± 1.47 mSv) was used as reference standard in all studies to evaluate diagnostic accuracy of LD- (1.22 ± 0.34 mSv) and ULD-CT (0.22 ± 0.05 mSv), respectively. LD-CT had high sensitivities for detection of bronchiectasis (82-96%), honeycomb (75-100%), and varying sensitivities for nodules (63-99%) and ground glass opacities (GGO) (77-91%). ULD-CT had high sensitivities for GGO (93-100%), pneumothorax (100%), consolidations (90-100%), and varying sensitivities for nodules (60-100%) and emphysema (65-90%).

CONCLUSION

The included studies found LD-CT to have high diagnostic accuracy in detection of honeycombing and bronchiectasis and ULD-CT to have high diagnostic accuracy for pneumothorax, consolidations and GGO. Summarizing evidence on diagnostic accuracy of LD- and ULD-CT for other chest pathology was not possible due to varying outcome measures, lack of precision estimates and heterogeneous study design and methodology.

Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning

PURPOSE

To conduct a simplified lesion-detection task of a low-dose (LD) PET-CT protocol for frequent lung screening using 30% of the effective PETCT dose and to investigate the feasibility of increasing clinical value of low-statistics scans using machine learning.

METHODS

We acquired 33 SD PET images, of which 13 had actual LD (ALD) PET, and simulated LD (SLD) PET images at seven different count levels from the SD PET scans. We employed image quality transfer (IQT), a machine learning algorithm that performs patch-regression to map parameters from low-quality to high-quality images. At each count level, patches extracted from 23 pairs of SD/SLD PET images were used to train three IQT models - global linear, single tree, and random forest regressions with cubic patch sizes of 3 and 5 voxels. The models were then used to estimate SD images from LD images at each count level for 10 unseen subjects. Lesion-detection task was carried out on matched lesion-present and lesion-absent images.

RESULTS

LD PET-CT protocol yielded lesion detectability with sensitivity of 0.98 and specificity of 1. Random forest algorithm with cubic patch size of 5 allowed further 11.7% reduction in the effective PETCT dose without compromising lesion detectability, but underestimated SUV by 30%.

CONCLUSION

LD PET-CT protocol was validated for lesion detection using ALD PET scans. Substantial image quality improvement or additional dose reduction while preserving clinical values can be achieved using machine learning methods though SUV quantification may be biased and adjustment of our research protocol is required for clinical use.

Baseline Chest Computed Tomography as Standard of Care in High-Risk Hematology Patients

Baseline chest computed tomography (BCT) in high-risk hematology patients allows for the early diagnosis of invasive pulmonary aspergillosis (IPA). The distribution of BCT implementation in hematology departments and impact on outcome is unknown. A web-based questionnaire was designed. International scientific bodies were invited. The estimated numbers of annually treated hematology patients, chest imaging timepoints and techniques, IPA rates, and follow-up imaging were assessed. In total, 142 physicians from 43 countries participated. The specialties included infectious diseases (n = 69; 49%), hematology (n = 68; 48%), and others (n = 41; 29%). BCT was performed in 57% (n = 54) of 92 hospitals. Upon the diagnosis of malignancy or admission, 48% and 24% performed BCT, respectively, and X-ray was performed in 48% and 69%, respectively. BCT was more often used in hematopoietic cell transplantation and in relapsed acute leukemia. European centers performed BCT in 59% and non-European centers in 53%. Median estimated IPA rate was 8% and did not differ between BCT (9%; IQR 5–15%) and non-BCT centers (7%; IQR 5–10%) (p = 0.69). Follow-up computed tomography (CT) for IPA was performed in 98% (n = 90) of centers. In high-risk hematology patients, baseline CT is becoming a standard-of-care. Chest X-ray, while inferior, is still widely used. Randomized, controlled trials are needed to investigate the impact of BCT on patient outcome.

A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Video-assisted thoracoscopic surgery for invasive pulmonary fungal infection in haematology patients

Background

Invasive pulmonary fungal infection in haematological patients sometimes was a difficult problem in diagnosis and treatments. This retrospective study was intended to assess the outcomes of video-assisted thoracoscopic surgery (VATS) in the treatments of this problem.

Methods

From January 2011 to December 2017, a total of 51 haematological patients underwent VATS for invasive pulmonary fungal infection. We collected and then analyzed potential factors including general conditions, types of haematological diseases, preoperative clinical symptoms, surgical procedures, length of postoperative hospital stay, incidence of postoperative complications and postoperative follow-ups.

Results

Of the 51 patients, 32 patients underwent video-assisted thoracoscopic wedge resection (62.7%), 6 patients underwent video-assisted thoracoscopic segmentectomy (11.8%) and 13 patients underwent video-assisted thoracoscopic lobectomy (25.5%). The mean operative time was 110.24±38.12 min. The average intraoperative blood loss was 112.35±87.85 mL. The mean postoperative hospital stay was 7.75±3.27 days. Prolonged air leak was found in 6 patients (11.8%), followed by excessive effusion which was found in 4 patients (7.8%). No life-threatening complications or resurgence of fungal infection occurred after surgery. Twenty-seven patients (52.9%) received postoperative antifungal therapies. No 30-day mortality and pulmonary fungal infection recurrence occurred in 6 to 24 months follow-ups.

Conclusions

VATS is an effective and safe option in management of invasive pulmonary fungal infection among patients with haematological diseases.

A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images

Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients' FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.

Diagnostic work up to assess early response indicators in invasive pulmonary aspergillosis in adult patients with haematologic malignancies

In immunocompromised patients with acute leukaemia as well as in allogeneic hematopoietic stem cell transplant patients, pulmonary lesions are commonly seen. Existing guidelines provide useful algorithms for diagnostic procedures and treatment options, but they do not give recommendations on how to evaluate early success or failure and if or when it is best to change therapy. Here, we review the diagnostic techniques currently used in association with clinical findings and propose an approach using a combination of computer tomography, clinical and all available biomarkers and inflammation parameters, especially those positive at baseline, to assess early response in invasive pulmonary aspergillosis. Computed tomography scans should be carried out at regular intervals during early and long-term follow-up. Imaging on day seven, or even earlier in clinically unstable patients, combined with an additional testing of biomarkers and inflammatory markers in between, is needed for a reliable assessment at day 14. If no improvement is seen after 2 weeks of therapy or the clinical condition is deteriorating, a change of antifungal therapy should be considered. Alleged breakthrough infections or treatment failure should undergo early diagnostic workup, including tissue biopsies when possible, to retrieve fungal cultures for resistance testing.

Radiation dose reduction techniques for chest CT: Principles and clinical results

Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.

Imaging Cancer Metabolism

It is widely accepted that altered metabolism contributes to cancer growth and has been described as a hallmark of cancer. Our view and understanding of cancer metabolism has expanded at a rapid pace, however, there remains a need to study metabolic dependencies of human cancer in vivo. Recent studies have sought to utilize multi-modality imaging (MMI) techniques in order to build a more detailed and comprehensive understanding of cancer metabolism. MMI combines several in vivo techniques that can provide complementary information related to cancer metabolism. We describe several non-invasive imaging techniques that provide both anatomical and functional information related to tumor metabolism. These imaging modalities include: positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) that uses hyperpolarized probes and optical imaging utilizing bioluminescence and quantification of light emitted. We describe how these imaging modalities can be combined with mass spectrometry and quantitative immunochemistry to obtain more complete picture of cancer metabolism. In vivo studies of tumor metabolism are emerging in the field and represent an important component to our understanding of how metabolism shapes and defines cancer initiation, progression and response to treatment. In this review we describe in vivo based studies of cancer metabolism that have taken advantage of MMI in both pre-clinical and clinical studies. MMI promises to advance our understanding of cancer metabolism in both basic research and clinical settings with the ultimate goal of improving detection, diagnosis and treatment of cancer patients.