Regulatory T Cells Exhibit Distinct Features in Human Breast Cancer.

Regulatory T (Treg) cells reside in lymphoid organs and barrier tissues where they control different types of inflammatory responses. Treg cells are also found in human cancers, and studies in animal models suggest that they contribute to cancer progression. However, properties of human intratumoral Treg cells and those present in corresponding normal tissue remain largely unknown. Here, we analyzed features of Treg cells in untreated human breast carcinomas, normal mammary gland, and peripheral blood. Tumor-resident Treg cells were potently suppressive and their gene-expression pattern resembled that of normal breast tissue, but not of activated peripheral blood Treg cells. Nevertheless, a number of cytokine and chemokine receptor genes, most notably CCR8, were upregulated in tumor-resident Treg cells in comparison to normal tissue-resident ones. Our studies suggest that targeting CCR8 for the depletion of tumor-resident Treg cells might represent a promising immunotherapeutic approach for the treatment of breast cancer.

A Single miRNA-mRNA Interaction Affects the Immune Response in a Context- and Cell-Type-Specific Manner.

MicroRNA (miRNA)-dependent regulation of gene expression confers robustness to cellular phenotypes and controls responses to extracellular stimuli. Although a single miRNA can regulate expression of hundreds of target genes, it is unclear whether any of its distinct biological functions can be due to the regulation of a single target. To explore in vivo the function of a single miRNA-mRNA interaction, we mutated the 3' UTR of a major miR-155 target (SOCS1) to specifically disrupt its regulation by miR-155. We found that under physiologic conditions and during autoimmune inflammation or viral infection, some immunological functions of miR-155 were fully or largely attributable to the regulation of SOCS1, whereas others could be accounted only partially or not at all by this interaction. Our data suggest that the role of a single miRNA-mRNA interaction is dependent on cell type and biological context.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.

OBJECTIVES: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

Local Targeting of Lung-Tumor-Associated Macrophages with Pulmonary Delivery of a CSF-1R Inhibitor for the Treatment of Breast Cancer Lung Metastases.

The lungs are major sites of metastases for several cancer types, including breast cancer (BC). Prognosis and quality of life of BC patients that develop pulmonary metastases are negatively impacted. The development of strategies to slow the growth and relieve the symptoms of BC lung metastases (BCLM) is thus an important goal in the management of BC. However, systemically administered first line small molecule chemotherapeutics have poor pharmacokinetic profiles and biodistribution to the lungs and significant off-target toxicity, severely compromising their effectiveness. In this work, we propose the local delivery of add-on immunotherapy to the lungs to support first line chemotherapy treatment of advanced BC. In a syngeneic murine model of BCLM, we show that local pulmonary administration (p.a.) of PLX-3397 (PLX), a colony-stimulating factor 1 receptor inhibitor (CSF-1Ri), is capable of overcoming physiological barriers of the lung epithelium, penetrating the tumor microenvironment (TME), and decreasing phosphorylation of CSF-1 receptors, as shown by the Western blot of lung tumor nodules. That inhibition is accompanied by an overall decrease in the abundance of protumorigenic (M2-like) macrophages in the TME, with a concomitant increase in the amount of antitumor (M1-like) macrophages when compared to the vehicle-treated control. These effects with PLX (p.a.) were achieved using a much smaller dose (1 mg/kg, every other day) compared to the systemic doses typically used in preclinical studies (40-800 mg/kg/day). As an additive in combination with intravenous (i.v.) administration of paclitaxel (PTX), PLX (p.a.) leads to a decrease in tumor burden without additional toxicity. These results suggested that the proposed immunochemotherapy, with regional pulmonary delivery of PLX along with the i.v. standard of care chemotherapy, may lead to new opportunities to improve treatment, quality of life, and survival of patients with BCLM.

TREG Cells in Cancer: Beyond Classical Immunological Control.

A prerequisite for tumor evolution toward a malignant state is the establishment of cell intrinsic and extrinsic mechanisms of immune suppression (Hanahan and Weinberg, 2000, 2011; Schreiber, Old, and Smyth, 2011). Widespread recruitment of Foxp3+ regulatory T cells (TREG) is a prevailing means to dampen antitumor immunity. Advances in the characterization of TREG cell heterogeneity and physiological function of tissue resident TREG cells unfold new possibilities for nontraditional tumor-promoting functions of intratumoral TREG cells. This review will focus on the nonclassical function of TREG cells and their implicancies for cancer biology and treatment.

Genes that mediate breast cancer metastasis to the brain.

The molecular basis for breast cancer metastasis to the brain is largely unknown. Brain relapse typically occurs years after the removal of a breast tumour, suggesting that disseminated cancer cells must acquire specialized functions to take over this organ. Here we show that breast cancer metastasis to the brain involves mediators of extravasation through non-fenestrated capillaries, complemented by specific enhancers of blood-brain barrier crossing and brain colonization. We isolated cells that preferentially infiltrate the brain from patients with advanced disease. Gene expression analysis of these cells and of clinical samples, coupled with functional analysis, identified the cyclooxygenase COX2 (also known as PTGS2), the epidermal growth factor receptor (EGFR) ligand HBEGF, and the alpha2,6-sialyltransferase ST6GALNAC5 as mediators of cancer cell passage through the blood-brain barrier. EGFR ligands and COX2 were previously linked to breast cancer infiltration of the lungs, but not the bones or liver, suggesting a sharing of these mediators in cerebral and pulmonary metastases. In contrast, ST6GALNAC5 specifically mediates brain metastasis. Normally restricted to the brain, the expression of ST6GALNAC5 in breast cancer cells enhances their adhesion to brain endothelial cells and their passage through the blood-brain barrier. This co-option of a brain sialyltransferase highlights the role of cell-surface glycosylation in organ-specific metastatic interactions.

Endogenous human microRNAs that suppress breast cancer metastasis.

A search for general regulators of cancer metastasis has yielded a set of microRNAs for which expression is specifically lost as human breast cancer cells develop metastatic potential. Here we show that restoring the expression of these microRNAs in malignant cells suppresses lung and bone metastasis by human cancer cells in vivo. Of these microRNAs, miR-126 restoration reduces overall tumour growth and proliferation, whereas miR-335 inhibits metastatic cell invasion. miR-335 regulates a set of genes whose collective expression in a large cohort of human tumours is associated with risk of distal metastasis. miR-335 suppresses metastasis and migration through targeting of the progenitor cell transcription factor SOX4 and extracellular matrix component tenascin C. Expression of miR-126 and miR-335 is lost in the majority of primary breast tumours from patients who relapse, and the loss of expression of either microRNA is associated with poor distal metastasis-free survival. miR-335 and miR-126 are thus identified as metastasis suppressor microRNAs in human breast cancer.

Mediators of vascular remodelling co-opted for sequential steps in lung metastasis.

Metastasis entails numerous biological functions that collectively enable cancerous cells from a primary site to disseminate and overtake distant organs. Using genetic and pharmacological approaches, we show that the epidermal growth factor receptor ligand epiregulin, the cyclooxygenase COX2, and the matrix metalloproteinases 1 and 2, when expressed in human breast cancer cells, collectively facilitate the assembly of new tumour blood vessels, the release of tumour cells into the circulation, and the breaching of lung capillaries by circulating tumour cells to seed pulmonary metastasis. These findings reveal how aggressive primary tumorigenic functions can be mechanistically coupled to greater lung metastatic potential, and how such biological activities may be therapeutically targeted with specific drug combinations.

Lymph node-targeting adjuvant/neoantigen-codelivering vaccines for combination glioblastoma radioimmunotherapy.

Glioblastoma multiforme (GBM) is the most common and lethal type of adult brain cancer. Current GBM standard of care, including radiotherapy, often ends up with cancer recurrence, resulting in limited long-term survival benefits for GBM patients. Immunotherapy, such as immune checkpoint blockade (ICB), has thus far shown limited clinical benefit for GBM patients. Therapeutic vaccines hold great potential to elicit anti-cancer adaptive immunity, which can be synergistically combined with ICB and radiotherapy. Peptide vaccines are attractive for their ease of manufacturing and stability, but their therapeutic efficacy has been limited due to poor vaccine co-delivery and the limited ability of monovalent antigen vaccines to prevent tumor immune evasion. To address these challenges, here, we report GBM radioimmunotherapy that combines radiotherapy, ICB, and multivalent lymph-node-targeting adjuvant/antigen-codelivering albumin-binding vaccines (AAco-AlbiVax). Specifically, to codeliver peptide neoantigens and adjuvant CpG to lymph nodes (LNs), we developed AAco-AlbiVax based on a Y-shaped DNA scaffold that was site-specifically conjugated with CpG, peptide neoantigens, and albumin-binding maleimide-modified Evans blue derivative (MEB). As a result, these vaccines elicited antitumor immunity including neoantigen-specific CD8+ T cell responses in mice. In orthotopic GBM mice, the combination of AAco-AlbiVax, ICB, and fractionated radiation enhanced GBM therapeutic efficacy. However, radioimmunotherapy only trended more efficacious over radiotherapy alone. Taken together, these studies underscore the great potential of radioimmunotherapy for GBM, and future optimization of treatment dosing and scheduling would improve the therapeutic efficacy.

The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.

In Vivo Imaging to Measure Spontaneous Lung Metastasis of Orthotopically-injected Breast Tumor Cells.

Metastasis remains the primary cause of cancer-related death. The succession of events that characterize the metastatic cascade presents multiple opportunities for therapeutic intervention, and the ability to accurately model them in mice is critical to evaluate their effects. Here, a step-by-step protocol is presented for the establishment of orthotopic primary breast tumors and the subsequent monitoring of the establishment and growth of metastatic lesions in the lung using in vivo bioluminescence imaging. This methodology allows for the evaluation of treatment or its biological effects along the entire range of metastatic development, from primary tumor escape to outgrowth in the lungs. Breast orthotopic tumors are generated in mice via injection of a luciferase-labeled cell suspension in the 4th mammary gland. Tumors are allowed to grow and disseminate for a specific amount of time and are then surgically resected. Upon resection, spontaneous lung metastasis is detected, and the growth over time is monitored using in vivo bioluminescence imaging. At the desired experimental endpoint, lung tissue can be collected for downstream analysis. The treatment of established, clinically evident metastasis is critical to improve outcomes for stage IV cancer patients, and it can be evaluated through tail vein models of experimental lung metastasis. However, metastatic dissemination occurs early in breast cancer, and many patients have latent, subclinical disseminated disease after surgery. Utilization of spontaneous models such as this one provides the opportunity to study the whole spectrum of the disease, especially the systemic effects driven by treatment of the primary tumor such as pre-metastatic niche priming, and evaluate treatments on dormant and subclinical disease after surgery.

Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.

Background and purpose: Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods: A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion: No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net.

Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist's performance for T staging?

PURPOSE: To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. METHODS: We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. RESULTS: The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720-0.839 for combined model and AUC = 0.697, 95% CI = 0.538-0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621-0.777 for combined model and AUC = 0.628, 95% CI = 0.558-0.689 for CT staging only, Boot CI = 0.099). CONCLUSION: CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.

Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer.

PURPOSE: Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. RESULTS: Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features. CONCLUSION: Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models.

Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer.

BACKGROUND AND PURPOSE: Manual delineation of head and neck tumor contours for radiomics analyses is tedious and time consuming. This study investigates if fast or readily available tumor contours can substitute full tumor contours by an experienced observer for an MR-based radiomics model to predict locoregional control (LRC) in oropharyngeal squamous cell carcinoma (OPSCC) tumors. MATERIALS AND METHODS: Radiomic features were extracted from postcontrast T1-weighted MRIs of 177 OPSCC primary tumors using six different manual delineation strategies. LRC prediction models based on recursive feature elimination combined with logistic regression were built. Models were trained and tested on data from each separate delineation. Additionally, the model derived from segmentations from the experienced reader was tested by each of the alternative delineations. Complementary, this was repeated with removal of size and shape features. Model performance was evaluated using area under the curve (AUC). RESULTS: Prediction performance of the experienced radiologist tumor delineation (AUC: 0.74) was superior compared to all other delineations when trained and tested (AUCs: 0.41-0.56) or trained on experienced delineations and tested (AUC: 0.56-0.67) on alternative segmentations. Removal of size and shape features considerably decreases prediction performance (AUC: 0.54). Applying the model based on expert delineations to spherical or single slice delineations makes prediction worthless since these models predict one class. CONCLUSION: Fast or readily available contours cannot substitute full expert tumor delineations in radiomics models predictive of LRC in OPSCC.

Genes that mediate breast cancer metastasis to lung.

By means of in vivo selection, transcriptomic analysis, functional verification and clinical validation, here we identify a set of genes that marks and mediates breast cancer metastasis to the lungs. Some of these genes serve dual functions, providing growth advantages both in the primary tumour and in the lung microenvironment. Others contribute to aggressive growth selectively in the lung. Many encode extracellular proteins and are of previously unknown relevance to cancer metastasis.

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning.

BACKGROUND AND PURPOSE: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semi-automatic approach for tumor segmentation that is expected to save time in the clinic. MATERIALS AND METHODS: We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performance using different combinations of MRI sequences as input. Finally, a semi-automatic approach by two human observers defining clipboxes around the tumor was tested. Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). RESULTS: The 3D UNet trained with full context and all sequences as input yielded a median Dice of 0.55, HD of 8.7 mm and MSD of 2.7 mm. Combining all MRI sequences was better than using single sequences. The semi-automatic approach with all sequences as input yielded significantly better performance (p < 0.001): a median Dice of 0.74, HD of 4.6 mm and MSD of 1.2 mm. CONCLUSION: Reducing the amount of context around the tumor and combining multiple MRI sequences improved the segmentation performance. A semi-automatic approach was accurate and clinically feasible.

Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models.

OBJECTIVES: New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors. METHODS: 177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup. RESULTS: A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction. CONCLUSION: Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS.

Prognostic functional MR imaging parameters in head and neck squamous cell carcinoma: A systematic review.

OBJECTIVE: Functional MR imaging has demonstrated potential for predicting treatment response. This systematic review gives an extensive overview of the current level of evidence for pre-treatment MR-based perfusion and diffusion imaging parameters that are prognostic for treatment outcome in head and neck squamous cell carcinoma (HNSCC) (PROSPERO registrationCRD42020210689). MATERIALS AND METHODS: According to the PRISMA statements, Medline, Embase and Scopus were queried for articles with a maximum date of October 19th, 2020. Studies investigating the predictive performance of pre-treatment MR-based perfusion and/or diffusion imaging parameters in HNSCC treatment response were included. All prognosticators were extracted from the primary tumor. Risk of bias was assessed using the QUIPS tool. Results were summarized in tables and forest plots. RESULTS: 31 unique studies met the inclusion criteria; among them, 11 articles described perfusion (n = 529 patients) and 28 described diffusion (n = 1626 patients) MR-imaging, eight studies were included in both categories. Higher Ktrans and Kep were associated with better treatment response for OS and DFS, respectively. Study findings for Vp and Ve were inconsistent or not significant. High-level controversy was observed between studies examining the MR diffusion parameters mean and median ADC. CONCLUSION: For HNSCC patients, the accurate and consistent results of pre-treatment MR-based perfusion parameters Ktrans and Kep are potential for clinical applicability predictive of OS and DFS and treatment decision guidance. Significant heterogeneity in study designs might affect high discrepancy in study results for parameters extracted from diffusion imaging. Furthermore, recommendations for future research were summarized.

Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer.

BACKGROUND: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables. METHODS: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC). RESULTS: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors. CONCLUSION: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.