Towards radiologist-level cancer risk assessment in CT lung screening using deep learning

PURPOSE

Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models.

METHODS

Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets.

RESULTS AND CONCLUSIONS

The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.

Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise

Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension.

An unsupervised learning approach to identify novel signatures of health and disease from multimodal data

BACKGROUND

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease signatures.

METHODS

We collected 1385 data features from diverse modalities, including metabolome, microbiome, genetics, and advanced imaging, from 1253 individuals and from a longitudinal validation cohort of 1083 individuals. We utilized a combination of unsupervised machine learning methods to identify multimodal biomarker signatures of health and disease risk.

RESULTS

Our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers. Stratification of individuals based on the signatures of these biomarkers identified distinct subsets of individuals with similar health statuses. Subset membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and body mass index. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We identified potential early signatures for hypertension and a poor metabolic health outcome. Additionally, we found novel associations between a uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family.

CONCLUSIONS

Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages-an essential step towards personalized, preventative health risk assessment.

Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes-these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

Separation of extra- and intracellular metabolites using hyperpolarized (13)C diffusion weighted MR

This work demonstrates the separation of extra- and intracellular components of glycolytic metabolites with diffusion weighted hyperpolarized (13)C magnetic resonance spectroscopy. Using b-values of up to 15,000smm(-2), a multi-exponential signal response was measured for hyperpolarized [1-(13)C] pyruvate and lactate. By fitting the fast and slow asymptotes of these curves, their extra- and intracellular weighted diffusion coefficients were determined in cells perfused in a MR compatible bioreactor. In addition to measuring intracellular weighted diffusion, extra- and intracellular weighted hyperpolarized (13)C metabolites pools are assessed in real-time, including their modulation with inhibition of monocarboxylate transporters. These studies demonstrate the ability to simultaneously assess membrane transport in addition to enzymatic activity with the use of diffusion weighted hyperpolarized (13)C MR. This technique could be an indispensible tool to evaluate the impact of microenvironment on the presence, aggressiveness and metastatic potential of a variety of cancers.

Sparsity-Based Pixel Super Resolution for Lens-Free Digital In-line Holography

Lens-free digital in-line holography (LDIH) is a promising technology for portable, wide field-of-view imaging. Its resolution, however, is limited by the inherent pixel size of an imaging device. Here we present a new computational approach to achieve sub-pixel resolution for LDIH. The developed method is a sparsity-based reconstruction with the capability to handle the non-linear nature of LDIH. We systematically characterized the algorithm through simulation and LDIH imaging studies. The method achieved the spatial resolution down to one-third of the pixel size, while requiring only single-frame imaging without any hardware modifications. This new approach can be used as a general framework to enhance the resolution in nonlinear holographic systems.

Dynamic UltraFast 2D EXchange SpectroscopY (UF-EXSY) of hyperpolarized substrates

In this work, we present a new ultrafast method for acquiring dynamic 2D EXchange SpectroscopY (EXSY) within a single acquisition. This technique reconstructs two-dimensional EXSY spectra from one-dimensional spectra based on the phase accrual during echo times. The Ultrafast-EXSY acquisition overcomes long acquisition times typically needed to acquire 2D NMR data by utilizing sparsity and phase dependence to dramatically undersample in the indirect time dimension. This allows for the acquisition of the 2D spectrum within a single shot. We have validated this method in simulations and hyperpolarized enzyme assay experiments separating the dehydration of pyruvate and lactate-to-pyruvate conversion. In a renal cell carcinoma cell (RCC) line, bidirectional exchange was observed. This new technique revealed decreased conversion of lactate-to-pyruvate with high expression of monocarboxylate transporter 4 (MCT4), known to correlate with aggressive cancer phenotypes. We also showed feasibility of this technique in vivo in a RCC model where bidirectional exchange was observed for pyruvate-lactate, pyruvate-alanine, and pyruvate-hydrate and were resolved in time. Broadly, the technique is well suited to investigate the dynamics of multiple exchange pathways and applicable to hyperpolarized substrates where chemical exchange has shown great promise across a range of disciplines.

Directly detected (55)Mn MRI: application to phantoms for human hyperpolarized (13)C MRI development

In this work we demonstrate for the first time directly detected manganese-55 ((55)Mn) magnetic resonance imaging (MRI) using a clinical 3T MRI scanner designed for human hyperpolarized (13)C clinical studies with no additional hardware modifications. Due to the similar frequency of the (55)Mn and (13)C resonances, the use of aqueous permanganate for large, signal-dense, and cost-effective "(13)C" MRI phantoms was investigated, addressing the clear need for new phantoms for these studies. Due to 100% natural abundance, higher intrinsic sensitivity, and favorable relaxation properties, (55)Mn MRI of aqueous permanganate demonstrates dramatically increased sensitivity over typical (13)C phantom MRI, at greatly reduced cost as compared with large (13)C-enriched phantoms. A large sensitivity advantage (22-fold) was demonstrated. A cylindrical phantom (d=8 cm) containing concentrated aqueous sodium permanganate (2.7 M) was scanned rapidly by (55)Mn MRI in a human head coil tuned for (13)C, using a balanced steady state free precession acquisition. The requisite penetration of radiofrequency magnetic fields into concentrated permanganate was investigated by experiments and high frequency electromagnetic simulations, and found to be sufficient for (55)Mn MRI with reasonably sized phantoms. A sub-second slice-selective acquisition yielded mean image signal-to-noise ratio of ~60 at 0.5 cm(3) spatial resolution, distributed with minimum central signal ~40% of the maximum edge signal. We anticipate that permanganate phantoms will be very useful for testing HP (13)C coils and methods designed for human studies.

Kinetic and perfusion modeling of hyperpolarized (13)C pyruvate and urea in cancer with arbitrary RF flip angles

The accurate detection and characterization of cancerous tissue is still a major problem for the clinical management of individual cancer patients and for monitoring their response to therapy. MRI with hyperpolarized agents is a promising technique for cancer characterization because it can non-invasively provide a local assessment of the tissue metabolic profile. In this work, we measured the kinetics of hyperpolarized [1-(13)C] pyruvate and (13)C-urea in prostate and liver tumor models using a compressed sensing dynamic MRSI method. A kinetic model fitting method was developed that incorporated arbitrary RF flip angle excitation and measured a pyruvate to lactate conversion rate, Kpl, of 0.050 and 0.052 (1/s) in prostate and liver tumors, respectively, which was significantly higher than Kpl in healthy tissues [Kpl =0.028 (1/s), P<0.001]. Kpl was highly correlated to the total lactate to total pyruvate signal ratio (correlation coefficient =0.95). We additionally characterized the total pyruvate and urea perfusion, as in cancerous tissue there is both existing vasculature and neovascularization as different kinds of lesions surpass the normal blood supply, including small circulation disturbance in some of the abnormal vessels. A significantly higher perfusion of pyruvate (accounting for conversion to lactate and alanine) relative to urea perfusion was seen in cancerous tissues (liver cancer and prostate cancer) compared to healthy tissues (P<0.001), presumably due to high pyruvate uptake in tumors.

Quantitative measurement of cancer metabolism using stimulated echo hyperpolarized carbon-13 MRS

PURPOSE

Magnetic resonance spectroscopy of hyperpolarized substrates allows for the observation of label exchange catalyzed by enzymes providing a powerful tool to investigate tissue metabolism and potentially kinetics in vivo. However, the accuracy of current methods to calculate kinetic parameters has been limited by T1 relaxation effects, extracellular signal contributions, and reduced precision at lower signal-to-noise ratio.

THEORY AND METHODS

To address these challenges, we investigated a new modeling technique using metabolic activity decomposition-stimulated echo acquisition mode. The metabolic activity decomposition-stimulated echo acquisition mode technique separates exchanging from nonexchanging metabolites providing twice the information as conventional techniques.

RESULTS

This allowed for accurate measurements of rates of conversion and of multiple T1 values simultaneously using a single acquisition.

CONCLUSION

The additional measurement of T1 values for the reaction metabolites provides further biological information about the cellular environment of the metabolites. The new technique was investigated through simulations and in vivo studies of transgenic mouse models of cancer demonstrating improved assessments of kinetic rate constants and new T1 relaxation value measurements for hyperpolarized (13) C-pyruvate, (13) C-lactate, and (13) C-alanine.

A rapid method for direct detection of metabolic conversion and magnetization exchange with application to hyperpolarized substrates

In this work, we present a new MR spectroscopy approach for directly observing nuclear spins that undergo exchange, metabolic conversion, or, generally, any frequency shift during a mixing time. Unlike conventional approaches to observe these processes, such as exchange spectroscopy (EXSY), this rapid approach requires only a single encoding step and thus is readily applicable to hyperpolarized MR in which the magnetization is not replenished after T(1) decay and RF excitations. This method is based on stimulated-echoes and uses phase-sensitive detection in conjunction with precisely chosen echo times in order to separate spins generated during the mixing time from those present prior to mixing. We are calling the method Metabolic Activity Decomposition Stimulated-echo Acquisition Mode or MAD-STEAM. We have validated this approach as well as applied it in vivo to normal mice and a transgenic prostate cancer mouse model for observing pyruvate-lactate conversion, which has been shown to be elevated in numerous tumor types. In this application, it provides an improved measure of cellular metabolism by separating [1-(13)C]-lactate produced in tissue by metabolic conversion from [1-(13)C]-lactate that has flowed into the tissue or is in the blood. Generally, MAD-STEAM can be applied to any system in which spins undergo a frequency shift.