Molecular immuno-imaging improves tumor detection in head and neck cancer.

Detection and accurate delineation of tumor is important for the management of head and neck squamous cell carcinoma (HNSCC) but is challenging with current imaging techniques. In this study, we evaluated whether molecular immuno-imaging targeting myeloperoxidase (MPO) activity, an oxidative enzyme secreted by many myeloid innate immune cells, would be superior in detecting tumor extent compared to conventional contrast agent (DTPA-Gd) in a carcinogen-induced immunocompetent HNSCC murine model and corroborated in human surgical specimens. In C57BL/6 mice given 4-nitroquinoline-N-oxide (4-NQO), there was increased MPO activity in the head and neck region as detected by luminol bioluminescence compared to that of the control group. On magnetic resonance imaging, the mean enhancing volume detected by the MPO-targeting agent (MPO-Gd) was higher than that by the conventional agent DTPA-Gd. The tumor volume detected by MPO-Gd strongly correlated with tumor size on histology, and higher MPO-Gd signal corresponded to larger tumor size found by imaging and histology. On the contrary, the tumor volume detected by DTPA-Gd did not correlate as well with tumor size on histology. Importantly, MPO-Gd imaging detected areas not visualized with DTPA-Gd imaging that were confirmed histopathologically to represent early tumor. In human specimens, MPO was similarly associated with tumors, especially at the tumor margins. Thus, molecular immuno-imaging targeting MPO not only detects oxidative immune response in HNSCC, but can better detect and delineate tumor extent than nonselective imaging agents. Thus, our findings revealed that MPO imaging could improve tumor resection as well as be a useful imaging biomarker for tumor progression, and potentially improve clinical management of HNSCC once translated.

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

Angiotensin II drives the production of tumor-promoting macrophages.

Macrophages frequently infiltrate tumors and can enhance cancer growth, yet the origins of the macrophage response are not well understood. Here we address molecular mechanisms of macrophage production in a conditional mouse model of lung adenocarcinoma. We report that overproduction of the peptide hormone Angiotensin II (AngII) in tumor-bearing mice amplifies self-renewing hematopoietic stem cells (HSCs) and macrophage progenitors. The process occurred in the spleen but not the bone marrow, and was independent of hemodynamic changes. The effects of AngII required direct hormone ligation on HSCs, depended on S1P(1) signaling, and allowed the extramedullary tissue to supply new tumor-associated macrophages throughout cancer progression. Conversely, blocking AngII production prevented cancer-induced HSC and macrophage progenitor amplification and thus restrained the macrophage response at its source. These findings indicate that AngII acts upstream of a potent macrophage amplification program and that tumors can remotely exploit the hormone's pathway to stimulate cancer-promoting immunity.

Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.

OBJECTIVE: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm. METHODS: This dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up. Training and testing datasets were acquired from two institutions. Quantitative 3D radiomics analyses were performed on nephrographic phase CT images. Ten-fold cross-validated LASSO regression was applied to the training dataset to identify the most discriminative features. A logistic regression model was trained to classify malignancy and tested on the independent dataset. Reported metrics included areas under the receiver operating characteristic curves (AUC) and balanced accuracy. Decision curve analysis for stratifying patients for surgery was performed in the testing dataset. A decision algorithm was built by combining consensus radiological readings of Bosniak categories and radiomics-based risks. RESULTS: A total of 149 CRL (139 patients; 65 years [56-72]) were included in the training dataset-35 Bosniak(B)-IIF (8.6% malignancy), 23 B-III (43.5%), and 23 B-IV (87.0%)-and 50 CRL (46 patients; 61 years [51-68]) in the testing dataset-12 B-IIF (8.3%), 10 B-III (60.0%), and 9 B-IV (100%). The machine learning model achieved high diagnostic performance in predicting malignancy in the testing dataset (AUC = 0.96; balanced accuracy = 94%). There was a net benefit across threshold probabilities in using the clinical decision algorithm over management guidelines based on Bosniak categories. CONCLUSION: CT-based radiomics modeling accurately distinguished benign from malignant CRL, outperforming the Bosniak classification. The decision algorithm best stratified lesions for surgery and active surveillance. KEY POINTS: • The radiomics model achieved excellent diagnostic performance in identifying malignant cystic renal lesions in an independent testing dataset (AUC = 0.96). • The machine learning-enhanced decision algorithm outperformed the management guidelines based on the Bosniak classification for stratifying patients to surgical ablation or active surveillance.

Can activated titanium interbody cages accelerate or enhance spinal fusion? a review of the literature and a design for clinical trials.

While spinal interbody cage options have proliferated in the past decade, relatively little work has been done to explore the comparative potential of biomaterial technologies in promoting stable fusion. Innovations such as micro-etching and nano-architectural designs have shown purported benefits in in vitro studies, but lack clinical data describing their optimal implementation. Here, we critically assess the pre-clinical data supportive of various commercially available interbody cage biomaterial, topographical, and structural designs. We describe in detail the osteointegrative and osteoconductive benefits conferred by these modifications with a focus on polyetheretherketone (PEEK) and titanium (Ti) interbody implants. Further, we describe the rationale and design for two randomized controlled trials, which aim to address the paucity of clinical data available by comparing interbody fusion outcomes between either PEEK or activated Ti lumbar interbody cages. Utilizing dual-energy computed tomography (DECT), these studies will evaluate the relative implant-bone integration and fusion rates achieved by either micro-etched Ti or standard PEEK interbody devices. Taken together, greater understanding of the relative osseointegration profile at the implant-bone interface of cages with distinct topographies will be crucial in guiding the rational design of further studies and innovations.

PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma.

PURPOSE: To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (OPSCC). METHODS: We analyzed 435 primary tumors (326 for training, 109 for validation) and 741 metastatic cervical lymph nodes (518 for training, 223 for validation) using FDG-PET and non-contrast CT from a multi-institutional and multi-national cohort. Utilizing 1037 radiomics features per imaging modality and per lesion, we trained, optimized, and independently validated machine-learning classifiers for prediction of HPV association in primary tumors, lymph nodes, and combined "virtual" volumes of interest (VOI). PET-based models were additionally validated in an external cohort. RESULTS: Single-modality PET and CT final models yielded similar classification performance without significant difference in independent validation; however, models combining PET and CT features outperformed single-modality PET- or CT-based models, with receiver operating characteristic area under the curve (AUC) of 0.78, and 0.77 for prediction of HPV association using primary tumor lesion features, in cross-validation and independent validation, respectively. In the external PET-only validation dataset, final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes, and an AUC of 0.73 for a virtual VOI combining all lymph nodes. CONCLUSION: We found that PET-based radiomics signatures yielded similar classification performance to CT-based models, with potential added value from combining PET- and CT-based radiomics for prediction of HPV status. While our results are promising, radiomics signatures may not yet substitute tissue sampling for clinical decision-making.

Spot and Diffuse Signs: Quantitative Markers of Intracranial Hematoma Expansion at Dual-Energy CT.

Purpose To compare dual-energy CT with iodine quantification to single-energy CT for evaluation of the spot sign for intracranial hematoma expansion. Materials and Methods In this retrospective study, 42 patients (mean age, 66 years ± 15 [standard deviation]; 19 women) were referred for dual-energy CT assessment of intracranial hemorrhage from October 2014 to January 2017. A machine learning approach (naive Bayes classifier) was used to identify iodine markers of extravasation for risk of hematoma expansion. Specificity and sensitivity of these markers were then independently validated in 65 new patients from February 2017 to February 2018. Results Analysis of dual-energy CT images identified two features of iodine extravasation: total iodine concentration within the hematoma (Ih) and focal iodine concentration in the brightest spot in the hematoma (Ibs) as predictors of expansion. The I2 score derived from these features provided a measure of expansion probability. Optimal classification threshold was an I2 score of 20 (95% confidence interval [CI]: 18, 23), leading to correct identification of 39 of 46 (85%; 95% CI: 71%, 94%) of the hematomas on the training set (sensitivity of 79% [11 of 14; 95% CI: 57%, 100%] and specificity of 88% [28 of 32; 95% CI: 76%, 99%]), and 62 of 70 (89%; 95% CI: 79%, 95%) of the hematomas on the validation set (sensitivity of 71% [10 of 14; 95% CI: 48%, 95%] and specificity of 93% [52 of 56; 95% CI: 86%, 100%]). Sensitivity, specificity, and accuracy of conventional spot sign were, respectively, 57% (eight of 14), 90% (29 of 32), and 80% (37 of 46) on the training set and 57% (eight of 14), 83% (47 of 56), and 75% (53 of 70) on the validation set. Conclusion This study identified two quantitative markers of intracranial hemorrhage expansion at dual-energy CT of the brain. The I2 score derived from these markers highlights the utility of dual-energy CT measurements of iodine content for high sensitivity risk assessment. © RSNA, 2018 Online supplemental material is available for this article.

Image-based biomarkers for solid tumor quantification.

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. METHODS: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

Differentiation of lymphomatous, metastatic, and non-malignant lymphadenopathy in the neck with quantitative diffusion-weighted imaging: systematic review and meta-analysis.

PURPOSE: To perform a systematic review and meta-analysis of literature comparing average apparent diffusion coefficient (ADC) for differentiating lymphomatous, metastatic, and non-malignant cervical lymphadenopathy. METHODS: We performed a comprehensive literature search of Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science Core Collection. Studies comparing average ADC of lymphomatous, metastatic, and non-malignant neck lymph nodes were included. The standardized mean difference and 95% confidence interval (CI) was calculated using random-effects models. In subgroup analysis of those studies applying ADC threshold for differentiation of cervical lymphadenopathy, pooled diagnostic odds ratio (DOR) and summary receiver operating characteristics (sROC) area under the curve (AUC) were determined. RESULTS: A total of 27 studies with 1165 patients were included, pooling data from 225 lymphomatous, 1162 metastatic, and 1333 non-malignant cervical lymph nodes. The average ADC values were lower in lymphomatous compared to metastatic nodes, and in metastatic compared to non-malignant nodes with a standardized mean difference of - 1.36 (95% CI: - 1.71 to - 1.01, p < 0.0001) and - 1.61 (95% CI: - 2.19 to - 1.04, p < 0.0001), respectively. In subgroup analysis, applying ADC threshold could differentiate lymphomatous from metastatic lymphadenopathy with DOR of 52.07 (95% CI 25.45-106.54) and sROC AUC of 0.936 (95% CI 0.896-0.979) and differentiate metastatic from non-malignant nodes with DOR of 39.45 (95% CI 16.92-92.18) and sROC AUC of 0.929 (95% CI 0.873-0.966). CONCLUSIONS: Quantitative assessment of ADC can help with differentiation of suspicious cervical lymph nodes, particularly in those patients without prior history of malignancy or unknown primary cancer site.

Styloid Process Osteoradionecrosis: Report of 3 Cases.

We report the imaging and clinical findings in 3 patients with osteoradionecrosis of the styloid process, an uncommon consequence of radiation therapy for head and neck malignancy.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

OBJECTIVE: There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. METHODS: Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. RESULTS: Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. CONCLUSIONS: Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. KEY POINTS: • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis.

Purpose To evaluate the associations among mathematical modeling with the use of magnetic resonance (MR) imaging-based texture features and deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and histologic high-grade endometrial carcinoma. Materials and Methods Institutional review board approval was obtained for this retrospective study. This study included 137 women with endometrial carcinomas measuring greater than 1 cm in maximal diameter who underwent 1.5-T MR imaging before hysterectomy between January 2011 and December 2015. Texture analysis was performed with commercial research software with manual delineation of a region of interest around the tumor on MR images (T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced images and apparent diffusion coefficient maps). Areas under the receiver operating characteristic curve and diagnostic performance of random forest models determined by using a subset of the most relevant texture features were estimated and compared with those of independent and blinded visual assessments by three subspecialty radiologists. Results A total of 180 texture features were extracted and ultimately limited to 11 features for DMI, 12 for LVSI, and 16 for high-grade tumor for random forest modeling. With random forest models, areas under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4% for DMI; 0.80, 80.9%, 72.5%, 76.6%, 74.3%, and 79.4% for LVSI; and 0.83, 81.0%, 76.8%, 78.1%, 60.7%, and 90.1% for high-grade tumor, respectively. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of visual assessment for DMI were 84.5%, 82.3%, 83.2%, 77.7%, and 87.8% (reader 3). Conclusion The mathematical models that incorporated MR imaging-based texture features were associated with the presence of DMI, LVSI, and high-grade tumor and achieved equivalent accuracy to that of subspecialty radiologists for assessment of DMI in endometrial cancers larger than 1 cm. However, these preliminary results must be interpreted with caution until they are validated with an independent data set, because the small sample size relative to the number of features extracted may have resulted in overfitting of the models. © RSNA, 2017 Online supplemental material is available for this article.

Dual-Energy CT: Balance Between Iodine Attenuation and Artifact Reduction for the Evaluation of Head and Neck Cancer.

OBJECTIVE: Dual-energy computed tomography high energy virtual monochromatic images (VMIs) can reduce artifact but suppress iodine attenuation in enhancing tumor. We investigated this trade-off to identify VMI(s) that strike the best balance between iodine detection and artifact reduction. METHODS: The study was performed using an Alderson radiation therapy phantom. Different iodine solutions (based on estimated tumor iodine content in situ using dual-energy computed tomography material decomposition) and different dental fillings were investigated. Spectral attenuation curves and quality index (QI: 1/SD) were evaluated. RESULTS: The relationship between iodine attenuation and QI depends on artifact severity and iodine concentration. For low to average concentration solutions degraded by mild to moderate artifact, the iodine attenuation and QI curves crossed at 95 keV. CONCLUSIONS: High energy VMIs less than 100 keV can achieve modest artifact reduction while preserving sufficient iodine attenuation and could represent a useful additional reconstruction for evaluation of head and neck cancer.

Low-Energy Virtual Monochromatic Dual-Energy Computed Tomography Images for the Evaluation of Head and Neck Squamous Cell Carcinoma: A Study of Tumor Visibility Compared With Single-Energy Computed Tomography and User Acceptance.

OBJECTIVE: Dual-energy computed tomography (CT) 40-keV virtual monochromatic images (VMIs) have been reported to improve visualization of head and neck squamous cell carcinoma, but a direct comparison to single-energy CT (SECT) is lacking, and there is debate regarding subjective user preference. We compared 40-keV VMIs with SECT and performed a subjective evaluation of their utility and acceptability for clinical use. METHODS: A total of 60 dual-energy CT and 60 SECT scans from 2 different institutions were evaluated. Tumor conspicuity was evaluated objectively using absolute and relative attenuation and subjectively by 3 head and neck specialists and 3 general radiologists. RESULTS: Tumors had significantly higher absolute and relative attenuation on 40-keV VMIs (P < 0.0001). Subjectively, the 40-keV VMIs improved visualization, with substantial (κ, 0.61-0.80) to almost perfect (κ, 0.81-1) interrater agreements. CONCLUSIONS: The 40-keV VMIs improve tumor visibility objectively and subjectively both by head and neck specialists and general radiologists.

Dual-Energy CT Characteristics of Parathyroid Adenomas on 25-and 55-Second 4D-CT Acquisitions: Preliminary Experience.

OBJECTIVE: The objective of this study was to compare the dual-energy computed tomography (CT) characteristics of parathyroid adenomas (PAs), thyroid tissue, and lymph nodes (LNs) and assess whether the spectral information can improve distinction of these tissues. METHODS: Dual-energy CT scans from 20 patients with pathologically proven PAs were retrospectively evaluated, identifying 19 eligible PAs and region of interest analysis used for spectral characterization. RESULTS: There was a significant difference in multiple spectral parameters between PAs, LNs, and the thyroid gland (P < 0.05-0.0001). The greatest difference in spectral characteristics of PAs compared with that of LNs was on the 25-second acquisition, whereas the 55-second acquisition was better for distinguishing PAs from the thyroid gland. CONCLUSIONS: Four-dimensional CT acquired in dual-energy CT mode has the potential to further enhance diagnostic accuracy for PA identification on individual phases of the perfusion study.

Computed tomography appearance of normal nonossified thyroid cartilage: implication for tumor invasion diagnosis.

OBJECTIVE: The objective of this study was to determine the density and homogeneity of the nonossified thyroid cartilage (NOTC) on contrast-enhanced computed tomography (CT) providing preliminary information for future evaluation of cartilage invasion using dual-energy CT. METHODS: One hundred normal-larynx CT scans were evaluated for the density and homogeneity of NOTC. RESULTS: The density of the NOTC was homogeneous in all cases. Nonossified thyroid cartilage had higher mean density than contiguous muscle, but there was overlap. In 47 cases, a lucent area was observed at the junction of the ossified and NOTC but not within the NOTC itself. In 11 cases, ossification was observed in only 1 cortex of the thyroid cartilage. Cartilage at the anterior commissure was not ossified in 7 cases. CONCLUSIONS: Nonossified thyroid cartilage has a homogeneous appearance on contrast-enhanced CT scans, but showed some normal variations that could be mistakenly reported as tumor invasion.

Origins of tumor-associated macrophages and neutrophils.

Tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) can control cancer growth and exist in almost all solid neoplasms. The cells are known to descend from immature monocytic and granulocytic cells, respectively, which are produced in the bone marrow. However, the spleen is also a recently identified reservoir of monocytes, which can play a significant role in the inflammatory response that follows acute injury. Here, we evaluated the role of the splenic reservoir in a genetic mouse model of lung adenocarcinoma driven by activation of oncogenic Kras and inactivation of p53. We found that high numbers of TAM and TAN precursors physically relocated from the spleen to the tumor stroma, and that recruitment of tumor-promoting spleen-derived TAMs required signaling of the chemokine receptor CCR2. Also, removal of the spleen, either before or after tumor initiation, reduced TAM and TAN responses significantly and delayed tumor growth. The mechanism by which the spleen was able to maintain its reservoir capacity throughout tumor progression involved, in part, local accumulation in the splenic red pulp of typically rare extramedullary hematopoietic stem and progenitor cells, notably granulocyte and macrophage progenitors, which produced CD11b(+) Ly-6C(hi) monocytic and CD11b(+) Ly-6G(hi) granulocytic cells locally. Splenic granulocyte and macrophage progenitors and their descendants were likewise identified in clinical specimens. The present study sheds light on the origins of TAMs and TANs, and positions the spleen as an important extramedullary site, which can continuously supply growing tumors with these cells.