Longitudinal CT Scans Research Articles

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow

PurposeAI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.Materials and methodsThe study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard.ResultsAI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80–0.84 vs. 0.81–0.82) and decreased inter-reader variability (median Dice 0.85–1.0 vs. 0.80–0.82; ICC 0.84 vs. 0.80), compared to manual segmentation.ConclusionThe findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes

BackgroundTo predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission. MethodsAn internal cohort for model development consists of 204 bacteriologically-confirmed TB patients who completed anti-tuberculosis treatment, with one pretreatment and two follow-up CT images (612 scans). Three radiomics feature-based models (RM) with multiple classifiers of Bagging, Random forest and Gradient boosting and two deep-learning-based models (i.e., supervised deep-learning model, SDLM; weakly supervised deep-learning model, WSDLM) are developed independently. Prediction scores of RM and deep-learning models with respectively highest performance are fused to create new fusion models under different fusion strategies. An additional independent validation was conducted on the external cohort comprising 80 patients (160 scans). ResultsFor RM scheme, 16 optimal radiomics features are finally selected using longitudinal scans. The AUCs of RM for Bagging, Random forest and Gradient boosting were 0.789, 0.773 and 0.764 in the internal cohort and 0.840, 0.834 and 0.816 in the external cohort, respectively. For deep learning-based scheme, AUCs of SDLM and WSDLM were 0.767 and 0.661 in the internal cohort, and 0.823 and 0.651 in the external. The fusion model yields AUCs from 0.767 to 0.802 in the internal cohort, and from 0.831 to 0.857 in the external cohort. ConclusionsFusion of radiomics features and deep-learning model may have the potential to predict early failure outcome of DR-TB, which may be combined to help prevent poor TB treatment outcomes.

Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients

Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients

Develop a Deep Radiomics Model for Predicting the Response to Neoadjuvant Chemoradiotherapy (nCRT) in Patients with Locally Advanced Esophageal Cancer Using Three-Stage Longitudinal CT Images

To develop a deep radiomics model for predicting the response to neoadjuvant chemoradiotherapy (nCRT) of patients with locally advanced esophageal cancer using three stage longitudinal CT images. In this study, 189 patients were used to train our model, 80 patients were used to test the performance of the trained model. All patients enrolled in this study underwent nCRT followed by esophagectomy, and all patients underwent three stage longitudinal CT scans (before nCRT, after nCRT and before esophagectomy). The number of radiomics features for each CT images was 2153, the number of longitudinal radiomic features for each patient was 6459. Then, we used the least absolute shrinkage and selection operator regression for feature importance analysis. After this, the selected features of each stage were feed to the disentangled representation network to explore the relationship between the dynamic changes of tumors before and after nCRT and the pathological complete response (pCR). In addition, in order to further evaluate the effect of the time interval between nCRT and esophagectomy on the response to nCRT, we conducted subgroup analysis on different time frames. The performance of our model was evaluated by area under curve (AUC), accuracy, sensitivity and specificity. Compared with only using single-stage CT images (the AUC of only using before nCRT CT images, after nCRT CT images, before esophagectomy CT images were 73.27%, 74.21%, and 74.95%, respectively), effectively exploring the dynamic changes of the tumor can achieve better performance in predicting the response to nCRT in the testing cohort (the AUC was 84.29%, 95% CI, 81.14%-87.44%). In addition, the performance of our proposed method outperforms any combinations of two stage CT images (the AUC using the CT images of before nCRT and after nCRT was 77.92%, the AUC using the CT images of before nCRT and before esophagectomy was 79.31%, the AUC using the CT images of after nCRT and before esophagectomy was 80.01%). Finally, the results showed that exploring the dynamic changes of the tumor using the three-stage CT images outperformed using single-stage CT images and any combinations of two-stage CT images in predicting the response to nCRT. The study also found that the time interval between nCRT and esophagectomy had some influence on the accuracy of pCR prediction, with the prediction accuracy tending to increase from 1 to 6 weeks and stabilizing after 6 weeks. By exploring the dynamic changes of tumors, the designed disentangled representation network can effectively predict the response to nCRT of patients with esophageal cancer. In addition, the time interval between nCRT and esophagectomy also has a certain impact on the response to nCRT.

Early and effective intracerebral hemorrhage evacuation is associated with a lower 1-year residual cavity volume and better functional outcomes

BackgroundWe explored the clinical significance of the residual hematoma cavity 1 year after minimally invasive intracerebral hemorrhage (ICH) evacuation.MethodsPatients presenting with spontaneous supratentorial ICH were evaluated for minimally invasive surgical...

Interstitial lung abnormalities in a large clinical lung cancer screening cohort: association with mortality and ILD diagnosis

BackgroundInterstitial lung abnormalities (ILA) are CT findings suggestive of interstitial lung disease in individuals without a prior diagnosis or suspicion of ILD. Previous studies have demonstrated that ILA are associated with clinically significant outcomes including mortality. The aim of this study was to determine the prevalence of ILA in a large CT lung cancer screening program and the association with clinically significant outcomes including mortality, hospitalizations, cancer and ILD diagnosis.MethodsThis was a retrospective study of individuals enrolled in a CT lung cancer screening program from 2012 to 2014. Baseline and longitudinal CT scans were scored for ILA per Fleischner Society guidelines. The primary analyses examined the association between baseline ILA and mortality, all-cause hospitalization, and incidence of lung cancer. Kaplan–Meier plots were generated to visualize the associations between ILA and lung cancer and all-cause mortality. Cox regression proportional hazards models were used to test for this association in both univariate and multivariable models.Results1699 subjects met inclusion criteria. 41 (2.4%) had ILA and 101 (5.9%) had indeterminate ILA on baseline CTs. ILD was diagnosed in 10 (24.4%) of 41 with ILA on baseline CT with a mean time from baseline CT to diagnosis of 4.47 ± 2.72 years. On multivariable modeling, the presence of ILA remained a significant predictor of death, HR 3.87 (2.07, 7.21; p < 0.001) when adjusted for age, sex, BMI, pack years and active smoking, but not of lung cancer and all-cause hospital admission. Approximately 50% with baseline ILA had progression on the longitudinal scan.ConclusionsILA identified on baseline lung cancer screening exams are associated with all-cause mortality. In addition, a significant proportion of patients with ILA are subsequently diagnosed with ILD and have CT progression on longitudinal scans.Trial registration number: ClinicalTrials.gov; No.: NCT04503044.

Steroids Therapy in Patients With Severe COVID-19: Association With Decreasing of Pneumonia Fibrotic Tissue Volume.

BackgroundWe use longitudinal chest CT images to explore the effect of steroids therapy in COVID-19 pneumonia which caused pulmonary lesion progression.Materials and MethodsWe retrospectively enrolled 78 patients with severe to critical COVID-19 pneumonia, among which 25 patients (32.1%) who received steroid therapy. Patients were further divided into two groups with severe and significant-severe illness based on clinical symptoms. Serial longitudinal chest CT scans were performed for each patient. Lung tissue was segmented into the five lung lobes and mapped into the five pulmonary tissue type categories based on Hounsfield unit value. The volume changes of normal tissue and pneumonia fibrotic tissue in the entire lung and each five lung lobes were the primary outcomes. In addition, this study calculated the changing percentage of tissue volume relative to baseline value to directly demonstrate the disease progress.ResultsSteroid therapy was associated with the decrease of pneumonia fibrotic tissue (PFT) volume proportion. For example, after four CT cycles of treatment, the volume reduction percentage of PFT in the entire lung was −59.79[±12.4]% for the steroid-treated patients with severe illness, and its p-value was 0.000 compared to that (−27.54[±85.81]%) in non-steroid-treated ones. However, for the patient with a significant-severe illness, PFT reduction in steroid-treated patients was −41.92[±52.26]%, showing a 0.275 p-value compared to −37.18[±76.49]% in non-steroid-treated ones. The PFT evolution analysis in different lung lobes indicated consistent findings as well.ConclusionSteroid therapy showed a positive effect on the COVID-19 recovery, and its effect was related to the disease severity.

The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system

SummaryThe respective value of clinical data and CT examinations in predicting COVID-19 progression is unclear, because the CT scans and clinical data previously used are not synchronized in time. To address this issue, we collected 119 COVID-19 patients with 341 longitudinal CT scans and paired clinical data, and we developed an AI system for the prediction of COVID-19 deterioration. By combining features extracted from CT and clinical data with our system, we can predict whether a patient will develop severe symptoms during hospitalization. Complementary to clinical data, CT examinations show significant add-on values for the prediction of COVID-19 progression in the early stage of COVID-19, especially in the 6th to 8th day after the symptom onset, indicating that this is the ideal time window for the introduction of CT examinations. We release our AI system to provide clinicians with additional assistance to optimize CT usage in the clinical workflow.

Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management

ObjectiveThe maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a “master curve” of AAA growth, and tests which parameters serve to enhance its predictability for clinical use. Methods106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization. ResultsAn exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates. ConclusionsThe exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.

The role of chest CT in management of asymptomatic SARS-CoV-2 infections: A longitudinal multi-center study in Chongqing, China.

Background: Multiple societies including the Fleischner Society do not recommend that CT is routinely used in asymptomatic SARS-CoV-2 infections; however, this advice is based on the limited evidence. In this study, we aim to confirm whether it is necessary to do CT scans in SARS-CoV-2 asymptomatic infections by summarizing the longitudinal chest CT and clinical features of asymptomatic SARS-CoV-2 infections.Methods: A total of 33 individuals (14 men and 19 women) with asymptomatic SARS-CoV-2 infections were retrospectively enrolled. Clinical data of CT positive and negative groups were compared. Longitudinal chest CT scans were reviewed for CT features and analyzed for temporal change.Results: Thirty-two (97%) individuals had positive results for first RT-PCR testing. For clinical data, only monocyte count showed a significant difference between CT positive and negative groups. For first chest CT, only eighteen (54.5%) individuals had abnormal manifestations, common CT features were GGO (88.9%) and consolidation (33.3%), the median number of segments involved was 3.0 (1.0-7.5). No case in CT negative group was abnormal on the follow-up CT. Three patterns of evolution throughout series of CT were observed in CT positive group, including gradual improvement (12, 66.7%), mismatch to improvement (3, 16.7%) and mild progression to improvement (3, 16.7%). On last CT scans, most cases had radiographic improvement but residual abnormalities. Significant differences were exhibited in density, long diameter, number of lung segments involved, and percentage of consolidation between the first and last CT scans. All cases had stable conditions and finally confirmed negative for SARS-CoV-2 RT-PCR tests without developing into severe pneumonia.Conclusion: Considering poor performance of CT in screening, stable conditions during followup, and good outcomes in asymptomatic SARS-CoV-2 infections, we confirm that it is unnecessary to do CT scans in asymptomatic SARS-CoV-2 infections.

Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage.

Objective: To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer. Methods: While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer. Results: The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods. Conclusion: The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.

Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks

ABSTRACT Automated segmentation of organs-at-risk (OAR) in follow-up images of the patient acquired during the course of treatment could greatly facilitate adaptive treatment planning in radiotherapy. Instead of segmenting each image separately, the segmentation could be improved by making use of the additional information provided by longitudinal data of previously segmented images of the same patient. We propose a tool for automated segmentation of longitudinal data that combines deformable image registration (DIR) and convolutional neural networks (CNN). The segmentation propagated by DIR from a previous image onto the current image and the segmentation obtained by a separately trained cross-sectional CNN applied to the current image are given as input to a longitudinal CNN, together with the images itself, that is trained to optimally predict the manual ground truth segmentation using all available information. Despite the fairly limited amount of training data available in this study, an improvement of the segmentation of five different OAR in head-and-neck CT scans was found compared to both the results of DIR and the cross-sectional CNN separately in terms of Dice coefficient.

Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning.

Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).

Personalized vaccine for advanced HCC.

TPS527 Background: The chronic inflammation and viral infection associated with HCC combined with liver tolerogenic mechanisms creates a profoundly immunosuppressive microenvironment. Modulating the microenvironment with checkpoint blockade benefits ~20% of patients. The non-responders and those with poor liver function are an unmet need. Checkpoint blockade requires effector cells to be resident within tumors. However, the majority of HCC lesions lack effector cells. Therefore, a strategy for amplifying the tumor-specific immune response, while counteracting the immunosuppressive mechanisms may provide an improved immunotherapy. Endogenous heat shock proteins (HSP) chaperone tumor neoantigens. A concentrated composition of calreticulin, hsp70, hsp90 and gr94/gp96 (CRCL) is purified from biopsy samples. In addition, a bioengineered allograft (BAG) derived from healthy blood donors with a Th1 memory phenotype, CD3/CD28-coated microbeads and high expression of CD40L has potent immunomodulatory and counter-regulatory properties. The combination of CRCL with BAG is being evaluated as a strategy to amplify tumor-specific immunity and counter-regulate the immunosuppressive microenvironment. Methods: An open-label Phase II clinical trial evaluating the safety and efficacy of CRCL+BAG vaccine in Child-Pugh A/B advanced/metastatic HCC w/o Sorafenib. Approximately 6-10 cores of tumor are collected at baseline. The tumor is lysed and CRCL purified using an isoelectric focusing technique. Subjects are primed with multiple intradermal injections of BAG cells alone to increase the titer of allo-specific Th1/Tc1 cells. Next, multiple intradermal injections of BAG+CRCL are administered to elicit increased titers of tumor-specific Th1/Tc1 cells. Subsequent intravenous infusions of BAG cells activate allo- and tumor-specific memory cells, enabling their infiltration into tumor lesions. The BAG allo-rejection response creates a type I cytokine storm which serves to down-regulate suppressor circuits. Response is evaluated by mRECIST and experimental biomarkers. Longitudinal CT scans with concurrent biopsies as well as multiple collections of PBMC and plasma permits immunomonitoring of these immune mechanisms. Clinical trial information: NCT02409524.

Phase I/IIb trial: In-situ vaccine for metastatic colorectal cancer.

TPS871 Background: Except for MSI-H tumors, CRC does not respond to immunotherapy. CRC does respond to the graft vs. tumor (GVT) immune effect that occurs after allogeneic stem cell transplantation. GVT is associated with GVHD toxicity which limits the clinical application. A bioengineered allograft (BAG) has been developed which can elicit host-mediated GVT-like effects without GVHD toxicity, chemotherapy conditioning or a HLA-matched donor. BAG are Th1 memory cells derived from blood of healthy donors with CD3/CD28 microbeads attached. These cells have immunomodulatory properties which enable modulation of Th1/Th2 balance and dysregulation of immunosuppressive circuits. We are evaluating the safety and efficacy of BAG in third-line mCRC. Methods: The study uses a standard 3+3 design followed by an expansion phase with the optimal dosing pattern. The protocol has four components: (A) priming; (B) in-situ vaccination; (C) extravasation and trafficking; and (D) counter immune suppression/avoidance. Priming involves intradermal injections of BAG cells which activates NK cells and develops allo-specific Th1/Tc1 immunity. In-Situ vaccination involves tumor cryoablation to release endogenous HSP which chaperone tumor neoantigens, followed immediately by the intralesional injection of BAG cells as adjuvant. Released HSP are engulfed and processed by immature dendritic cells (DC) attracted to the tissue damage. The inflammatory microenvironment created by the BAG and the subsequent allo-rejection response amplified by the priming induces DC maturation. These DC display processed tumor antigens on upregulated MHCI/II and express co-stimulatory CD80/86 enabling priming of a tumor-specific Th1/Tc1 response. CD40L and interferon-gamma expressed by BAG activates allo-specific and tumor-specific memory cells upon intravenous infusion, permitting trafficking to tumor. The host rejection of BAG releases endogenous danger signals creating a sustained systemic inflammatory cytokine release which serves to counter-regulate immunosuppressive mechanisms. Longitudinal CT scans biopsies, PBMC and serum samples are collected for analysis to verify immune events within each phase of the protocol. Clinical trial information: NCT02380443.

Airway tree reconstruction in expiration chest CT scans facilitated by information transfer from corresponding inspiration scans.

Analysis and comparison of airways imaged in pairs of inspiration and expiration lung CT scans provides important information for quantitative assessment of lung diseases like chronic obstructive pulmonary disease. However, airway tree reconstruction in expiration CT scans is a challenging problem. Typically, only a low number of airway branches are found in expiration scans, compared to inspiration scans. To detect more airways in expiration CT scans, the authors introduce a novel airway reconstruction approach and assess its performance. The method requires a pair of inspiration and expiration CT scans and utilizes information from the inspiration scan to facilitate reconstructing the airway tree in the expiration lung CT scan. First, an initial airway tree (high confidence) and airway candidates (limited confidence) are reconstructed in the expiration scan by utilizing a 3D graph-based reconstruction method. Then, the 3D airway tree is reconstructed in the inspiration scan. Second, correspondences between expiration and inspiration tree structures are identified by utilizing a novel hierarchical tree matching algorithm that utilizes a local CT image-based similarity criterion. Third, the tree information from the inspiration airway tree is used to select expiration candidates, resulting in the final expiration tree structure. The approach was evaluated on a diverse set of 40 scan pairs and compared to the baseline method, which utilizes only the expiration CT scan. The proposed method produced a significant (p < 0.05) increase in airway tree length by 13.35 cm, on average, which represents an 11.21% increase relative to the baseline result using only the expiration CT scan. A detailed analysis of all additionally identified airways resulted in a true and false positive rate of 94.8% and 5.2%, respectively. The true positive rate was found to be significantly higher than the false positive rate (p < 0.05). The proposed method allowed increasing the number of found airways in expiration scans significantly. In addition, the algorithm establishes correspondence between inspiration and expiration airway trees, which can facilitate label transfer between airway trees and quantitative assessment of change in airways. The approach can be adapted to facilitate airway reconstruction in several longitudinal lung CT scans by means of mutual information transfer.

Inclusion criteria for cancer cachexia clinical trials: CT-defined skeletal muscle loss versus body weight loss.

67 Background: Cancer cachexia is defined by skeletal muscle loss, with or without fat loss (Fearon et al 2011); however, inclusion criteria for cachexia clinical trials requires a defined weight loss over time rather than muscle loss. We hypothesized that cross sectional imaging may reveal the presence of cachexia otherwise obscured by fat mass changes. Methods: A retrospective analysis of longitudinal CT scans was performed in metastatic colorectal cancer (mCRC) patients screened for a cancer cachexia trial, which required ≥5% weight loss in the prior 6 mos. De-identified CT images were analyzed for total muscle, subcutaneous, and visceral fat cross-sectional areas (cm2) at the 3rd lumbar vertebra at baseline and up to 12 mos prior (Lieffers et al 2009). Logistic regression was used to test differences between patients with &lt;5% vs ≥5% weight loss. Random intercept regression was used to evaluate significant trends in CT measures over time. Results: 42 mCRC patients were screened and 3(7%) enrolled. Patients were excluded for comorbidity/contraindication 14 (33%), excessive [&gt;20%] weight loss 4 (9.5%), and insufficient [&lt;5%] weight loss 19 (45%). For the &lt;5% weight loss subset, there was a mean of 6.7 CT scans (SD=2.67) and of 9% (SD=5.4, min=0%, 25th percentile=4.9%) mean max muscle loss. Notably this group was simultaneously losing muscle (p=0.002) and gaining visceral adipose (p=0.007). For the ≥5% weight loss subset, there was a mean of 7.5 CT scans (SD=4.5) and 20% (SD=10.0, min=5.2%, 25th percentile =10.6) mean max muscle loss. Greater max muscle loss increased the odds of being in the ≥5% weight loss subset (OR=1.19, 95% CI: 1.06,1.33). This group also had a significant decrease in visceral adipose over time (p&lt;0.001). Redefined inclusion criteria of ≥5% muscle loss would have included 14 of the 19 patients excluded because of &lt;5% weight loss. Conclusions: Defining cancer cachexia as weight loss over time may be limited as it does not capture body composition changes and hinders trial accrual. Cross-sectional CT body composition analysis may improve early detection of muscle loss and improve trial accrual.

Development of novel approach to diagnostic imaging of lung cancer with 18F-Nifene PET/CT using A/J mice treated with NNK.

Development of novel methods of early diagnosis of lung cancer is one of the major tasks of contemporary clinical and experimental oncology. In this study, we utilized the tobacco nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)-induced lung cancer in A/J mice as an animal model for development of a new imaging technique for early diagnosis of lung cancer. Lung cancer cells in A/J mice overexpress nicotinic acetylcholine receptors. Longitudinal CT scans were carried out over a period of 8 months after NNK treatment, followed by PET/CT scans with 18F-Nifene that binds to α4-made nicotinic receptors with high affinity. PET/CT scans of lungs were also obtained ex vivo. CT revealed the presence of lung nodules in 8-month NNK-treated mice, while control mice had no tumors. Imaging of live animals prior to necropsy allowed correlation of results of tumor load via PET/CT and histopathological findings. Significant amount of 18F-Nifene was seen in the lungs of NNK-treated mice, whereas lungs of control mice showed only minor uptake of 18F-Nifene. Quantitative analysis of the extent and amount of 18F-Nifene binding in lung in vivo and ex vivo demonstrated a higher tumor/nontumor ratio due to selective labeling of tumor nodules expressing abundant α4 nicotinic receptor subunits. For comparison, we performed PET/CT studies with 18F-FDG, which is used for the imaging diagnosis of lung cancer. The tumor/nontumor ratios for 18F-FDG were lower than for 18F-Nifene. Thus, we have developed a novel diagnostic imaging approach to early diagnosis of lung cancer using 18F-Nifene PET/CT. This technique allows quantitative assessment of lung tumors in live mice, which is critical for establishing tumor size and location, and also has salient clinical implications.

TH‐A‐214‐06: Organ Dose Weighting Methods for Tube Current Modulated CT Exams: Demonstration Using Adult Patient Phantoms

Purpose: To demonstrate a method to estimate organ doses from tube current modulated (TCM) CT examinations using angular organ dose weighting. Method and Materials: An extensive set of Monte Carlo dose calcualtions has become available in recent years using realistic models of human anatomy. Dose to individual organs are documented for particular CT slice positions. For longitudinal TCM CT scans, the available slice‐wise dose is weighted by the tube current for that position. This method has been applied to the RPI series of pregnant phantoms at different genstational ages. Organ doses for x‐ray exposures in flat projections in the four primary directions are also available from tabulated data. To account for rotational TCM in organ dose calculations, a geometric combination of these flat projection organ doses is used to generate weighting factors for tube currents that vary with angle. Sample TCM CT scans have been constructed for standard CT scan ranges using the RPI‐Adult Male and RPI‐Adult Female phantoms. Results: Applicaiton of longitudanal TCM to scans of pregnant patients are shown to reduce fetal dose by 25–35% relative to uniform current at maximum patient thickness. The effective dose applying rotational TCM is reduced by 29% for chest scans and 37% for abdominal scans relative to constant‐current scans at those positions. These dose reductions are verified with direct Monte Carlo simulation of the applied TCM scheme. Conclusion: TCM use is known to result in significant dose savings to the patient. Due to the uniqueness of each individual tube current modulated CT scan, direct Monte Carlo simulations of every possible scheme are impractical. This study demonstrates a method to estimate organ doses from an arbitrary TCM scheme using existing CT dose data. This algorithm is being adopted for use in a CT dose software tool, VirtualDose.