Predictor Of Distant Metastasis Research Articles

EXPRESSION OF CONCERN: A small molecule, LLL12 inhibits constitutive STAT3 and IL-6-induced STAT3 signaling and exhibits potent growth suppressive activity in human multiple myeloma cells.

Expression of Concern: H. Wang, B. Wang, Q. Liao, H. An, W. Li, X. Jin, S. Cui, and L. Zhao, "Overexpression of RhoGDI, a novel predictor of distant metastasis, promotes cell proliferation and migration in hepatocellular carcinoma," FEBS Letters 588, no. 3 (2014): 503-508. https://doi.org/10.1016/j.febslet.2013.12.016. This Expression of Concern is for the above article, published online on 24 December 2013, in Wiley Online Library (wileyonlinelibrary.com), and has been issued by agreement between the journal Editor-in-Chief Michael Brunner; FEBS Press; and John Wiley and Sons Ltd. The Expression of Concern has been agreed due to concerns raised by a third party regarding the similarity of beta-actin blots in Fig. 3A between the HepG2 and the MHCC-97H lanes. The authors responded to an inquiry by the publisher and supplied what was labelled as original data. However, following an evaluation of the authors' response by FEBS and the editors, Wiley and the journal have determined that they are unable to verify if the files provided by the authors correspond to the research described in the article. The journal is issuing this Expression of Concern because the results of the experiment as presented cannot be confirmed. The authors do not agree with the expression of concern.

Lung adenocarcinoma size as a predictor of distant metastasis: A CT scan-based measurement.

Previous studies have associated tumor size with metastasis and prognosis in lung carcinoma; however, a precise cut-off for predicting distant metastasis in lung adenocarcinoma remains unclear. The aim of this study was to determine the cut-off point for predicting distant metastasis in lung adenocarcinoma. A cross-sectional study was conducted at Dr. Moewardi Hospital, Surakarta, Indonesia, from January 2022 to September 2023. Total sampling was employed, involving patients over 18 years old with a confirmed diagnosis of lung adenocarcinoma based on lung computed tomography (CT) scan findings, who had not yet received chemotherapy and had confirmed metastasis outside the lung. The study's dependent variable was the incidence of distant metastasis, while the independent variable was lung adenocarcinoma size. Two experienced thoracic radiologists measured lung adenocarcinoma size by assessing the longest axis using chest multi-slice computed tomography (MSCT) in the lung window setting. Receiver operating characteristic (ROC) curve analysis determined the optimal tumor size cut-off for predicting distant metastasis. Of 956 thoracic cancer patients, 108 were diagnosed with lung adenocarcinoma. After applying the inclusion and exclusion criteria, 89 patients were eligible. In the present study, tumor size predicted 68.1% of distant metastasis cases, with a cut-off point of 7.25 cm, yielding a sensitivity of 61.9% and a specificity of 61.5%. Tumors >7.25 cm had a 2.60-fold higher risk of distant metastasis compared to smaller tumors, with larger tumors more likely to spread to various sites. In conclusion, lung adenocarcinomas larger than 7.25 cm have a 2.60-fold increased risk of distant metastasis, making tumor size a crucial predictive factor. The study provides valuable insights for radiologists and can improve diagnosis accuracy and treatment planning by emphasizing tumor size as a key factor in managing lung adenocarcinoma.

Stimulated thyroglobulin and diagnostic 131-iodine whole-body scan as a predictor of distant metastasis and association with response to treatment in pediatric thyroid cancer patients.

Differentiated thyroid carcinoma (DTC) is a rare oncological disease in the pediatric population, presenting with a more aggressive form. Stimulated thyroglobulin (sTg) and the 131-iodine whole-body scans (WBSs) are known adult markers related to the presence of distant metastasis. Little is known about their roles in the pediatric population. To evaluate sTg levels and diagnostic WBS (DxWBS) as predictors of distant metastasis after thyroidectomy and to correlate with the response to treatment at the end of follow-up in pediatric DTC. Patients under 19 years old diagnosed with DTC from 1980 to 2022 were retrospectively evaluated. sTg values and WBS were assessed after thyroidectomy and prior radioiodine treatment (RIT) and correlated with the possibility of finding distant metastasis and response to treatment at the end of follow-up. In a total of 142 patients with a median age of 14.6 (4-18) years who were followed for 9.5 ± 7.2 years and classified according to the ATA risk of recurrence as low (28%), intermediate (16%), and high risk (56%), 127 patients had their sTg evaluated. A sTg value of 21.7 ng/dl yielded a sensitivity of 88% compared to 30% for DxWBS in predicting distant metastasis. Specificity was 60% and 100% respectively. 42% of patients obtained discordant results between DxWBS and RxWBS. In high-risk patients, sTg levels were particularly able to differentiate those who would have distant metastasis with better diagnostic accuracy than the WBSs. The sTg level had better performance in detecting distant metastases in pediatric DTC than the DxWBS. DxWBS's low performance suggests that caution should be taken in interpreting their findings in terms of the underdiagnosis for metastatic disease, especially when the sTg level already suggests distant disease.

SAT516 Impact Of The Braf V600E Gene Mutation In Patients With Papillary Thyroid Cancer From Northern Ecuadorian Andes

Abstract Disclosure: A.P. Solis Pazmiño: None. T. Cardenas: None. J. Cucalon: None. G. Jaramillo-Koupermann: None. G. Galvez: None. C. Reyes: None. J. Salazar- Vega: None. IntroductionThe incidence of thyroid cancer (TC) is increasing worldwide. Most new TC cases are papillary thyroid cancer (PTC). Ideally, the individualization of treatment decisions should not only be based on the context and values of the patient but also on the biology of the tumor. Among them, BRAF V600E + TERT has been associated with aggressive clinic-pathologic outcomes, including tumor recurrence and mortality. The aim of this study was to determine the prevalence of the BRAFV600E mutation in PTC patients from the northern Ecuadorian Andes.MethodsA retrospective cohort study of 193 tumors, was classified according to the TNM system (AJCC 7th edition) and the patients were managed based on the 2015 ATA guidelines from June 2016 to December 2019 in Hospital Eugenio Espejo, Quito-Ecuador. PTC genomic DNA from formalin-fixed, paraffin-embedded tissue was analyzed for exon 15 of the BRAF oncogene by Sanger Sequencing.ResultsFrom 193 patients included, 146 were positive for the mutation (75.6%), and 47 were wild type (24.4%). In the bivariate analysis, the mutation was significantly associated with the presence of distant metastasis (p = 0.02), and it was also a predictor of distant metastasis (odds ratio [OR], 4.1; 95% CI 1.5 - 10.8). There was no significant association between BRAF positivity with demographic variables (age, gender, BMI, altitude), or with tumor characteristics (tumor size, focality, aggressive/nonaggressive variant, lymph node metastases, ACJJ stage, thyroglobulin or iodotherapy values).ConclusionIt has been proposed that patients harboring the mutation be managed more aggressively in the early stages, however, we conclude that the impact of BRAF V600E on PTC is still controversial. To the best of our knowledge, this is the first report of its kind carried out in the Ecuadorian population. Studies involving massive sequencing and prospective clinical correlation are needed in the future. KEYWORDS: Papillary thyroid carcinoma, BRAF V600E mutation, distant metastasis. Presentation Date: Saturday, June 17, 2023

202. AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR DETECTION OF CIRCULATING TUMOR CELLS IN PATIENTS WITH ESOPHAGEAL CANCER

Abstract Background Circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response, and prognosis in various cancer. However, the overall process of detecting these marker expressions in CTCs becomes more complex and time consuming as more markers of cytopathologic heterogeneity are discovered. The aim of this study was to evaluate the use of a convolutional neural network (CNN)-based image processing artificial intelligence (AI) for CTC detection in ESCC patients. Methods We first investigated the accuracy of the AI algorithm to distinguish ESCC lines (KYSEs) from peripheral blood mononuclear cells (PBMCs). Then we used the AI algorithm to detect CTCs in peripheral blood samples obtained from ESCC patients. Results The AI algorithm discriminated KYSE from PBMCs of healthy volunteers accompanied by EpCAM staining with greater than 99.8% accuracy when the AI was trained on the same KYSE. The average accuracy of the AI and the 4 investigators in the differentiation of KYSE from PBMCs was 100% and 91.8%, respectively (p = 0.011). Average time to complete classification of 100 cell images for AI and human was 0.74 and 630.4 seconds, respectively (p = 0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI were 44.5 over 10 ESCC patients and 2.4 over 5 healthy volunteers, respectively (p = 0.019). Conclusion These results indicate that the CNN-based image-processing algorithm for CTC detection provides higher accuracy and shorter analysis time compared to human, suggesting its clinical application for ESCC patients.

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods. To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study. A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort. The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models. DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.

Prediction of Distant Metastases in Patients with Kidney Cancer Based on Gene Expression and Methylation Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive histological type of cancer in this location. Distant metastases are present in approximately 30% of patients at the time of first examination. Therefore, the ability to predict the occurrence of metastases in patients at early stages of the disease is an urgent task aimed at personalized treatment. Samples of tumor and paired histologically normal kidney tissue from patients with metastatic and non-metastatic ccRCC were studied. Gene expression was analyzed using real-time PCR. The level of gene methylation was evaluated using bisulfite conversion followed by quantitative methylation-specific PCR. Two groups of genes were analyzed in this study. The first group includes genes whose expression is significantly reduced during metastasis: CA9, NDUFA4L2, EGLN3, and BHLHE41 (p < 0.001, ROC analysis). The second group includes microRNA genes: MIR125B-1, MIR137, MIR375, MIR193A, and MIR34B/C, whose increased methylation levels are associated with the development of distant metastases (p = 0.002 to <0.001, ROC analysis). Based on the data obtained, a combined panel of genes was formed to identify patients whose tumors have a high metastatic potential. The panel can estimate the probability of metastasis with an accuracy of up to 92%.

Validation of the prognostic value of a three-gene signature and clinical parameters-based risk score in prostate cancer patients.

The study aimed to validate the prognostic value of the Prostatype® risk score (P-score), which includes a three-gene signature and conventional risk factors, in a retrospective cohort. All 716 patients diagnosed with prostate cancer from 2008 to 2010 at Skåne University Hospital, Sweden, were included. After excluding patients based on pathological and clinical eligibility criteria, RNA quality, and presence of metastases at diagnosis, a final cohort comprising 316 patients was further analyzed. Expression levels of three genes (IGFBP3, F3, and VGLL3) were measured in archived formalin-fixed paraffin-embedded core needle biopsies. The gene expression data were combined with clinical parameters (Gleason score, prostate-specific antigen, and clinical tumor stage) to calculate the P-score for each patient. Predictive performance of the P-score in terms of prostate cancer-specific mortality (PCSM), distant metastasis and adverse pathological outcomes were investigated. The P-score predicted both PCSM (hazard ratio [HR] = 1.6) and metastasis (HR = 1.46). The P-score had an area under curve (AUC) of 0.93 when predicting the PCSM risk at 10 years (95% confidence interval [CI]: 0.89-0.98), which was significantly better than both D'Amico (AUC: 0.81, 95% CI: 0.72-0.90, p < 0.001) and UCSF-CAPRA (AUC: 0.88, 95% CI: 0.80-0.96, p < 0.05). Decision curve analysis showed a higher net benefit of the P-score compared to both D'Amico and CAPRA. All three risk scores performed similarly in the prediction of distant metastases. For patients who underwent radical prostatectomy (RP), a higher P-score correlated with adverse pathological features such as pathologic tumor stage T3-4 (p < 0.0001) and ≥International Society of Urological Pathology grade group 3 (p < 0.0001). Our findings provide evidence for the prognostic value of the P-score. The P-score predicted the risk for PCSM more accurately than the D'Amico and CAPRA scores. Performance was similar when predicting the risk for development of distant metastases within 10 years. Moreover, the P-score correlated with adverse pathological outcomes in RP specimens. Thus, the P-score could provide useful information for patients and their doctors to make informed decisions at the time of diagnosis.

The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer.

Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.

The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model

ObjectivesThis research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model by SHapley Additive exPlanations (SHAP) framework.MethodsFour powerful ML models were developed using data from male breast cancer (MBC) patients in the SEER database between 2010 and 2015 and MBC patients from our hospital between 2010 and 2020. The area under curve (AUC) and Brier score were used to assess the capacity of different models. The Delong test was applied to compare the performance of the models. Univariable and multivariable analysis were conducted using logistic regression.ResultsOf 2351 patients were analyzed; 168 (7.1%) had distant metastasis (M1); 117 (5.0%) had bone metastasis, and 71 (3.0%) had lung metastasis. The median age at diagnosis is 68.0 years old. Most patients did not receive radiotherapy (1723, 73.3%) or chemotherapy (1447, 61.5%). The XGB model was the best ML model for predicting M1 in MBC patients. It showed the largest AUC value in the tenfold cross validation (AUC:0.884; SD:0.02), training (AUC:0.907; 95% CI: 0.899—0.917), testing (AUC:0.827; 95% CI: 0.802—0.857) and external validation (AUC:0.754; 95% CI: 0.739—0.771) sets. It also showed powerful ability in the prediction of bone metastasis (AUC: 0.880, 95% CI: 0.856—0.903 in the training set; AUC: 0.823, 95% CI:0.790—0.848 in the test set; AUC: 0.747, 95% CI: 0.727—0.764 in the external validation set) and lung metastasis (AUC: 0.906, 95% CI: 0.877—0.928 in training set; AUC: 0.859, 95% CI: 0.816—0.891 in the test set; AUC: 0.756, 95% CI: 0.732—0.777 in the external validation set). The AUC value of the XGB model was larger than that of nomogram in the training (0.907 vs 0.802) and external validation (0.754 vs 0.706) sets.ConclusionsThe XGB model is a better predictor of distant metastasis among MBC patients than other ML models and nomogram; furthermore, the XGB model is a powerful model for predicting bone and lung metastasis. Combining with SHAP values, it could help doctors intuitively understand the impact of each variable on outcome.

Predictors of Distant Metastasis and Prognosis in Newly Diagnosed T1 Intrahepatic Cholangiocarcinoma.

According to American Joint Committee on Cancer (AJCC) 8th staging system, T1 intrahepatic cholangiocarcinoma (T1 ICC) is considered a tumor with no vascular invasion. However, T1 ICC usually occurs distant metastasis (DM), and the clinical features of these patients could help clinicians identify the high-risk population. We reviewed 1959 newly diagnosed patients with T1 ICC from the Surveillance, Epidemiology, and End Results (SEER) database during 2004-2018. Logistic regression models and Cox proportional hazards models were conducted to predict the risk of DM and overall survival (OS), respectively, and then, web-based nomograms were constructed. Decision curve analysis (DCA) and clinical impact curves (CIC) were used to measure the clinical utility of the models. The low-, medium-, and high-risk groups were identified by calculating the summary of the risk points. Nomograms on the web were also created to help clinicians better use these prediction models. Tumor size and lymph node metastasis accounted for the first two largest proportions among the DM nomogram scores, while surgery, DM, age at diagnosis, chemotherapy, and lymph node metastasis occupied the largest percentage in OS nomogram. DM nomogram was established for these newly diagnosed patients with T1 ICC, and OS nomogram was developed to visually predict the OS rate of 3, 5, and 10 years. The calibration curves revealed a valid predictive accuracy of nomograms, of which the C-index was 0.703 and 0.740, respectively, for good discrimination. DCAs, CICs, and risk subgroups showed the clinical validity of these nomograms. Two websites were created to make it easier to use these nomograms. Novel web-based nomograms predicting the risk of DM and OS for T1 ICC were constructed. These predictive tools might help clinicians make precise clinical strategies for each patient with T1 ICC.

EVALUATION OF PRE-ABLATION NLR AND LMR AS PREDICTORS OF DISTANT METASTASES IN PATIENTS WITH DIFFERENTIATED THYROID CANCER.

This research aim was to evaluates the role of the pre-ablation neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) as predictors of distant metastases in patients with differentiated thyroid cancer (DTC). A retrospective analysis was given to 140 patients with DTC who received 131I remnant ablation after surgery. The patients were divided into two groups based on the existence of distant metastasis. The two groups showed no significant difference in age, gender, WBCs, neutrophils, monocytes, eosinophils, basophils and whether the tumor was multifocal. In the univariate analysis, significant differences were found in tumor size (p=0.021), lymphocyte (p=0.012), NLR (p=0.027), and LMR (p=0.007). According to the ROC curves, NLR had an AUC of 0.612 ± 0.097 with a cut-off value of 1.845, sensitivity of 60.0%, and specificity of 66.2% (p=0.027). LMR had an AUC of 0.638 ± 0.095 with a cut-off value of 4.630, sensitivity of 84.6%, and specificity of 35.4% (p=0.007). In the multivariate analysis, larger tumor size (OR=5.246, 95% CI 1.269-10.907, p=0.009) and higher NLR (OR=2.087, 95% CI 0.977-4.459, p=0.034) were statistically significant for distant metastases. This research reveals that pre-ablation NLR and tumor size are significantly statistically correlated with distant metastases in patients with DTC.

Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC: Development and External Validation of a Multi-Institutional Model

IntroductionDistant metastases (DMs) are the primary driver of mortality for patients with early stage NSCLC receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population. MethodsWe used a multi-institutional database of 1280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006 to 2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared with a random survival forests model. The higher performing model was evaluated on an external data set of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores. ResultsThe FG model yielded an AUC of 0.71 (95% confidence interval [CI]: 0.57–0.86) compared with the AUC of random survival forest at 0.69 (95% CI: 0.63–0.85) in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC of 0.70 (95% CI: 0.57–0.83) with good calibration (Brier score: 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% [3 of 15] versus 2.9% [7 of 241], p = 0.001) and external validation (21.4% [3 of 15] versus 7.8% [9 of 116], p = 0.095). A model nomogram and online application was made available. ConclusionsWe developed and externally validated a practical model that predicts DM risk in patients with NSCLC receiving SBRT which may help select patients for systemic therapy.

Abstract 5280: TERT promoter mutations are an independent predictor of distant metastasis in Middle Eastern papillary thyroid microcarcinoma

Abstract Background: Papillary thyroid microcarcinomas (PTMCs) have been attributed to the recent increased incidence of thyroid cancer. Although indolent, a subset of PTMC could potentially develop distant metastasis (DM). This study aimed to evaluate the clinico-pathological features and molecular characteristics of PTMC and identify the risk factors for DM in PTMC patients from Middle Eastern ethnicity. Methods: We retrospectively analyzed 210 patients with histologically confirmed PTMC. Clinico-pathological associations for DM, BRAF mutation and TERT mutation were analyzed successfully in 184 patients. Multivariate analysis was performed using Cox proportional hazards model and logistic regression analysis. Results: Among the PTMC patients included in this cohort, DM was noted in 6.0%(11/184), whereas tumor relapse occurred in 29/184 (15.8%). Of the 11 cases with DM, lung metastasis occurred in 8 cases, bone metastasis in 2 cases and brain metastasis in 1 case. Presence of extrathyroidal extension and older age were significantly associated with DM. Molecular analysis showed BRAF V600E mutations to be the most frequent, being detected in 45.7% (84/184). TERT promoter mutations were detected in 16 (8.7%) cases and were significantly associated with DM and shorter metastasis-free survival in multivariate analysis. Conclusions: Our study indicates a surprisingly high frequency of TERT promoter mutation in Saudi patients with PTMC. Identifying TERT promoter mutations as an independent predictor of DM in patients with microcarcinoma could explain the inherent aggressive nature of PTMC from Middle Eastern ethnicity and magnify its role in patient risk stratification, which might help in improving therapeutic strategy for these patients. Citation Format: Saeeda Omer Ahmed, Abdul K. Siraj, Sandeep K. Parvathareddy, Kaleem Iqbal, Zeeshan Qadri, Maha Al-Rasheed, Sarah Siraj, Saravanan Thangavel, Roxanne Diaz, Allianah Benito, Ingrid F. Victoria, Khawla S. AlKuraya. TERT promoter mutations are an independent predictor of distant metastasis in Middle Eastern papillary thyroid microcarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5280.

MRI-detected extramural vascular invasion is the most powerful predictor of systemic recurrence in locally advanced rectal cancer. Implications for total neoadjuvant strategies planning.

Background: Chemotherapy in locally advance rectal cancer (LARC) is shifting from an adjuvant setting to a total neoadjuvant therapy (TNT) strategy. However, we still lack easily available preoperative parameters truly identifying the group of patients at high risk of systemic recurrence. MRI-detected extramural vascular invasion (mrEMVI) is increasingly emerging as the best predictor of distant metastasis (DM) development. We hereby analyzed the relationship between mrEMVI, its pathological counterpart (pEMVI), and the development of DM in a single-center series of resected rectal cancers.

Hyaluronic Acid Correlates With Bone Metastasis and Predicts Poor Prognosis in Small-Cell Lung Cancer Patients.

BackgroundHyaluronan (HA) is one of the essential elements of the extracellular matrix (ECM), involved in the onset of metastasis in various tumors. The interaction and binding of the ligand–receptor HA/cluster of differentiation-44 (CD44) regulate the physical and biochemical properties of the ECM, which correlates with an increased propensity toward metastasis and poor survival outcome. Our study aimed to explore HA for predicting metastasis and survival rate in patients with small-cell lung cancer (SCLC).Materials and MethodsThis prospective cohort study recruited 72 patients with SCLC. Plasma HA and CD44 levels were assayed by enzyme-linked immunosorbent assay (ELISA) for 72 cases before initial systematic treatment (baseline samples), and plasma HA was detected via after-2-cycle-chemotherapy (A-2-C-CT) in 48 samples. Logistic regression analysis and the Cox proportional risk model were used to determine the independent predictors of distant metastasis and survival rate of patients.ResultsBaseline plasma HA was notably associated with bone metastasis (BM) [OR (95% CI = 1.015 (1.006–1.024), p = 0.001]. Multivariate logistic regression analysis showed that baseline plasma HA was chosen as an independent predictor of BM. Either baseline HA or CD44 or both were associated with BM. Dynamic alteration of HA was notably associated with A-2-C-CT clinical efficacy. Multivariate Cox regression analysis in forward likelihood ratio showed that A-2-C-CT HA was an independent predictor of progression-free survival (PFS) and overall survival (OS).ConclusionsHA appears to be used as an independent predictive factor for BM, and the dynamic detection of HA can predict prognosis in SCLC patients. The mechanism of the HA/CD44 axis in BM of SCLC deserves further exploration.

18F-FDG-PET/CT can be used to predict distant metastasis in hypopharyngeal squamous cell carcinoma

BackgroundHypopharyngeal squamous cell carcinoma (HPSCC) has a high rate of distant metastasis, resulting in poor prognosis. The role of the maximum standardized uptake value (SUVmax), which was assessed via pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG-PET), and computed tomography (CT) was examined, for predicting distant metastasis and survival.MethodsThis study included 121 patients who underwent pretreatment FDG-PET/CT scanning and subsequent treatment for HPSCC. The SUVmax was measured via FDG-PET/CT. A receiver operating characteristic (ROC) curve analysis was used to determine whether the SUVmax was a predictor of distant metastasis and to select the best cutoff value. Univariate and multivariate Cox hazard regression analyses were used in identifying associations between the SUVmax and other clinicopathological factors with distant metastasis-free survival.ResultsDistant metastases were identified in 33 patients during the median follow-up of 24 months after treatment. The ROC curve analysis determined that SUVmax was predictive of distant metastasis and identified a SUVmax of 13.9 as the best potential cutoff value. The univariate analysis showed that T and N classification, clinical stage, and SUVmax were significantly related to distant metastasis. However, in multivariate analysis, an SUVmax ≥ 13.9 was the only independent predictor of distant metastasis. Patients with high SUVmax values displayed significantly shorter distant metastasis-free survival and overall survival.ConclusionsSUVmax determined via pretreatment FDG-PET/CT is useful for predicting distant metastasis, distant metastasis-free survival, and overall survival in patients with HPSCC.Graphical

Морфологические особенности распределения компонентов экстрацеллюлярного матрикса - матриксной металлопротеиназы-9 и коллагена IV типа в метастазирующей меланоме хориоидеи

Introduction. Early diagnosis and prediction of distant metastases in choroidal melanoma patients based on morphological and IHC assessment of the components of the tumor microenvironment is a topical issue. The aim was to evaluate the IHC expression of MMP-9 and type IV collagen in metastatic choroidal melanoma. Materials and methods. We analyzed 43 cases of choroidal melanoma T2(a,b,c)-3N0M0-1 during 2013–2017. The patients were divided into two groups: group 1 included patients with primary choroidal melanoma without distant metastases (N=25) and group 2 included patients with primary choroidal melanoma with distant liver metastases (N=18). We performed an IHC assay of the surgical samples with MMP-9 (Poly) (MMP-9) and Collagen-IV (Clone CIV 22) (Col4) antibodies. Qualitative, semi-quantitative, and quantita-tive parameters were evaluated with digital pathology software. We developed a semi-quantitative method for estimating the intensity of IHC expression of these markers (in scores) in choroidal melanoma. Results. The study included 22 female and 21 male patients (51.2% and 48.8%, respectively), the average age being 60.5 years. The thickness of choroidal melanoma averaged 8.3 mm; the average diameter of the tumor was 12.3 mm. We detected strong MMP-9 expression in tumor cells and their microenvironment and remodeled collagen framework in tumors with pronounced tumor necrosis, thickness, and scleral involve-ment. A more pronounced expression of MMP-9 was associated with an increase in isolated type IV collagen fibers in the extracellular matrix (ECM) of the metastatic choroidal melanoma. Conclusion. During the growth and progression of choroidal melanoma, the components of the microenvi-ronment and tumor cells have a mutual effect with the disorganization of ECM components. Indicators of IHC expression (MMP-9, Col4) in a tumor should not be considered as a diagnostic marker of metastatic choroidal melanoma. However, they can be used in clinical practice when monitoring the course of the disease and in studying the mechanisms of remodeling of the tumor microenvironment. Keywords: choroidal melanoma, MMP-9, type IV collagen, metastasis

Oncological Outcomes in Patients with Appendicular Myxofibrosarcomas: A Retrospective Study.

Background Myxofibrosarcoma (MFS) is notorious for its infiltrative growth pattern, making wide excisions difficult to achieve. Our objective was to assess the impact of surgical margins and other factors that affected rates of local recurrence (LR), distant metastasis (DM), and overall survival (OS) of individuals undergoing resection for MFS. Methods We retrospectively reviewed the medical records of 209 patients with appendicular soft tissue sarcomas between January 2012 and June 2018. Of these, 29 patients (14%) were diagnosed with myxofibrosarcoma. These patients underwent a total of 33 resections. The pathological analyses were conducted by an experienced musculoskeletal (MSK) pathologist. Demographics data, operative details, adjuvant therapy, and oncological outcomes were assessed. Results Of the 29 patients (33 resections), the overall LR rate was 24% (7/29) and the 2-year LR rate was 17% (5/29). Factors associated with negative oncological outcomes were as follows: tumor size ≤10 cm (2-year local recurrence-free rates (LRFRs), 65%; 95% CI, 44–86%; p=0.02) and positive surgical margins grouped with surgical margins ≤0.1 cm (hazard ratio (HR), 11.74; 95% CI, 1.41–97.74; p=0.02). Chemotherapy and radiotherapy together increased the 2-year LRFR (LRFR, 100%; 95% CI, 100%, p=0.001). Two-year DM and OS rates were 15% and 79%, respectively. Female gender was a predictor of distant metastasis. Local recurrence had a negative impact on overall survival. Intraoperative analysis of resection margin accuracy was 75% (12/16) when non-MSK pathologists were involved but 100% accurate (12/12) when analyzed by an MSK pathologist. Conclusion Myxofibrosarcomas showed high LR rates after treatment. Close margins (≤0.1 cm) should be considered as a risk factor for LR, and LR is associated with negative overall survival. Neoadjuvant therapy in terms of combined chemotherapy and radiation therapy associates with decreased LR rates. If intraoperative assessment of margins is to be done, it should be performed by an experienced MSK pathologist.

Major Salivary Gland Cancer With Distant Metastasis Upon Presentation: Patterns, Outcomes, and Imaging Implications.

Given limited data availability on distant metastasis (DM) in major salivary gland (MSG) malignancy presentation, we aimed to evaluate the rate, histologic patterns, location, and predictors of DM at first MSG cancer presentation and suggest potential implications on diagnostic workup. Retrospective cohort. Commission on Cancer-accredited hospitals. We included patients in the National Cancer Database (2010-2016) with MSG malignancy. Site and rate of DM were stratified by histologic subtype. Factors predictive of DM at presentation were determined by multivariate regression analysis. Survival analyses were conducted via the Kaplan-Meier method, log-rank test, and Cox regression analysis. Of 5776 patients with MSG carcinoma, 333 (5.8%) presented with DM. The most common DM site was the lung (57.1%), followed by bone (46.8%) and liver (19.5%). DM was most common in adenocarcinoma-not otherwise specified (15.1%, 132/874) and salivary duct carcinoma (10.4%, 30/288). High-grade mucoepidermoid carcinoma had the highest rate of lung metastases (81.6%, 31/38). Conversely, myoepithelial carcinoma had the highest rate of bone metastases (85.7%, 6/7). DM at presentation was independently associated with an increased mortality risk (hazard ratio, 1.62; 95% CI, 1.40-1.90). We identified a DM rate of 5.8% in MSG malignancy at presentation. Overall 43% of patients presented without DM to the lung but with DM to the bones, liver, and/or brain. The most common metastatic sites differed by tumor histology. Staging with computed tomography neck and chest alone may fail to detect sites of DM; this work can be used for patient counseling in the clinical setting.