Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival. Results For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p  10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival. Conclusions Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC.publishedVersio

An mri-based radiomic prognostic index predicts poor outcome and specific genetic alterations in endometrial cancer

Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcriptional alterations associated with the RPI were investigated in the study cohort. Potential prognostic markers were further explored for validation in an mRNA validation cohort (n = 161). The RPI included four tumor texture features, and a high RPI was significantly associated with poor disease-specific survival in both the study cohort (p < 0.001) and the MRI validation cohort (p = 0.030). The association between RPI and gene expression profiles revealed 46 significantly differentially expressed genes in patients with a high RPI versus a low RPI (p < 0.001). The most differentially expressed genes, COMP and DMBT1, were significantly associated with disease-specific survival in both the study cohort and the mRNA validation cohort. In conclusion, a high RPI score predicts poor outcome and is associated with specific gene expression profiles in endometrial cancer patients. The promising link between radiomic tumor profiles and molecular alterations may aid in developing refined prognostication and targeted treatment strategies in endometrial cancer.publishedVersio

MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer

Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC). Methods Preoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model. Results iTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD <  6 mm with OR of 5.8 (p < 0.001) and lower figures for DOI ≥ 5 mm (OR = 2.8, p = 0.01), APD ≥ 17 mm (OR = 2.8, p < 0.001) and iDMI (OR = 1.1, p = 0.82). Patients with iTFD < 6 mm also had significantly reduced progression-free survival with hazard ratio of 2.4 (p < 0.001). Conclusion For predicting pDMI, iTFD yielded best diagnostic performance and iTFD < 6 mm outperformed other cutoff-based imaging markers and conventional subjective assessment of deep myometrial invasion (iDMI) for diagnosing pDMI. Thus, iTFD at MRI represents a promising preoperative imaging biomarker that may aid in predicting pDMI and high-risk disease in EC.publishedVersio

Stromal integrin α11 regulates PDGFR-β signaling and promotes breast cancer progression

Cancer-associated fibroblasts (CAFs) are key actors in modulating the progression of many solid tumors, such as breast cancer (BC). Herein, we identify an integrin α11/PDGFRβ–positive CAF subset displaying tumor-promoting features in BC. In the preclinical MMTV-PyMT mouse model, integrin α11 deficiency led to a drastic reduction of tumor progression and metastasis. A clear association between integrin α11 and PDGFRβ was found at both transcriptional and histological levels in BC specimens. High stromal integrin α11/PDGFRβ expression was associated with high grades and poorer clinical outcome in human BC patients. Functional assays using 5 CAF subpopulations (1 murine, 4 human) revealed that integrin α11 promotes CAF invasion and CAF-induced tumor cell invasion upon PDGF-BB stimulation. Mechanistically, the proinvasive activity of integrin α11 relies on its ability to interact with PDGFRβ in a ligand-dependent manner and to promote its downstream JNK activation, leading to the production of tenascin C, a proinvasive matricellular protein. Pharmacological inhibition of PDGFRβ and JNK impaired tumor cell invasion induced by integrin α11+ CAFs. Collectively, our study uncovers an integrin α11+ subset of protumoral CAFs that exploits the PDGFRβ/JNK signaling axis to promote tumor invasiveness in BC.publishedVersio

Influence of beam pruning techniques on LET and RBE in proton arc therapy

IntroductionProton arc therapy (PAT) is an emerging treatment modality that holds promise to improve target volume coverage and reduce linear energy transfer (LET) in organs at risk. We aimed to investigate if pruning the highest energy layers in each beam direction could increase the LET in the target and reduce LET in tissue and organs at risk (OAR) surrounding the target volume, thus reducing the relative biological effectiveness (RBE)-weighted dose and sparing healthy tissue.MethodsPAT plans for a germinoma, an ependymoma and a rhabdomyosarcoma patient were created in the Eclipse treatment planning system with a prescribed dose of 54 Gy(RBE) using a constant RBE of 1.1 (RBE1.1). The PAT plans was pruned for high energy spots, creating several PAT plans with different amounts of pruning while maintaining tumor coverage, denoted PX-PAT plans, where X represents the amount of pruning. All plans were recalculated in the FLUKA Monte Carlo software, and the LET, physical dose, and variable RBE-weighted dose from the phenomenological Rørvik (ROR) model and an LET weighted dose (LWD) model were evaluated.Results and discussionFor the germinoma case, all plans but the P6-PAT reduced the mean RBE-weighted dose to the surrounding healthy tissue compared to the PAT plan. The LET was increasingly higher within the PTV for each pruning iteration, where the mean LET from the P6-PAT plan was 1.5 keV/μm higher than for the PAT plan, while the P4- and P5-PAT plans provided an increase of 0.4 and 0.7 keV/μm, respectively. The other plans increased the LET by a smaller margin compared to the PAT plan. Likewise, the LET values to the healthy tissue were reduced for each degree of pruning. Similar results were found for the ependymoma and the rhabdomyosarcoma case. We demonstrated a PAT pruning technique that can increase both LET and RBE in the target volume and at the same time decreased values in healthy tissue, without affecting the target volume dose coverage

Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer

BACKGROUND To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients. MATERIALS AND METHODS Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age. RESULTS High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06). CONCLUSION CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies

Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer

BACKGROUND To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients. MATERIALS AND METHODS Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age. RESULTS High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06). CONCLUSION CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies

Preoperative tumor size at MRI predicts deep myometrial invasion, lymph node metastases, and patient outcome in endometrial carcinomas

OBJECTIVE: The aim of this study was to explore the relation between preoperative tumor size based on magnetic resonance imaging (MRI) and the surgical pathologic staging parameters (deep myometrial invasion, cervical stroma invasion, and metastatic lymph nodes) and to assess the prognostic impact of tumor size in endometrial carcinomas. Interobserver variability for the different tumor size measurements was also assessed. METHODS/MATERIALS: Preoperative pelvic MRI of 212 patients with histologically confirmed endometrial carcinomas was read independently by 3 radiologists. Maximum tumor diameters were measured in 3 orthogonal planes (anteroposterior, transverse, and craniocaudal planes [CC]), and tumor volumes were estimated. Tumor size was analyzed in relation to surgical staging results and patient survival. The multivariate analyses were adjusted for preoperative risk status based on endometrial biopsy. Intraclass correlation coefficients and receiver operating characteristics curves for the different tumor measurements were also calculated. RESULTS: Anteroposterior tumor diameter independently predicted deep myometrial invasion (P < 0.001), whereas CC tumor diameter tended to independently predict lymph node metastases (P = 0.06). Based on receiver operating characteristic curves, the following tumor size cutoff values were identified: anteroposterior diameter greater than 2 cm predicted deep myometrial invasion (unadjusted odds ratio [OR], 12.4; P < 0.001; adjusted OR, 6.7; P < 0.001) and CC diameter greater than 4 cm predicted lymph node metastases (unadjusted OR, 6.2; P < 0.001; adjusted OR, 4.9; P = 0.009). Large tumor size was associated with reduced progression/recurrence-free survival (P ≤ 0.005 for all size parameters), and CC diameter had an independent impact on survival (adjusted hazards ratio, 1.04; P = 0.009). The interobserver variability for the different size measurements was very low (intraclass correlation coefficient, 0.78-0.85). CONCLUSIONS: Anteroposterior tumor diameter greater than 2 cm predicts deep myometrial invasion, and CC tumor diameter greater than 4 cm predicts lymph node metastases. Tumor size is a strong prognostic factor in endometrial carcinomas. Preoperative tumor measurements based on MRI may potentially improve preoperative risk stratification models and thus enable better tailored surgical treatment in endometrial cancer