Enhanced staging of extrahepatic hepatocellular carcinoma metastasis through dual-tracer PET/computed tomography: a systematic review and meta-analysis.

The aim of this study was to quantify the diagnostic value of dual-tracer PET/computed tomography (CT) with 11C-acetate and fluorodeoxyglucose (FDG) in per-lesion and per-patient and its effect on clinical decision-making for choosing the most appropriate management. The study protocol is registered a priori at https://osf.io/rvm75/. PubMed, Web of Science, Embase, and Cochrane Library were searched for relevant studies until 1 June 2023. Studies regarding the review question were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess bias risk. Per-lesion and per-patient diagnostic performance were calculated for: (1) 11C-acetate alone; (2) FDG alone; and (3) dual tracer of 11C-acetate and FDG. A direct comparison of these three combinations was made. The possible sources of statistical heterogeneity were also examined. We also calculated the percentage change in clinical decision-making when dual-tracer PET/CT was added to conventional imaging routinely used for metastatic evaluation (CT/MRI). Grading of Recommendations, Assessment, Development, and Evaluations tool was used to evaluate the certainty of evidence. Eight studies including 521 patients and 672 metastatic lesions were included. Dual-tracer PET/CT had a per-lesion sensitivity of 96.3% [95% confidence interval (CI), 91.8-98.4%] and per-patient sensitivity of 95.5% (95% CI, 89.1-98.2%) which were highly superior to either of tracers alone. Per-patient specificity was 98.5% (84.1-99.9%) which was similar to either of tracers alone. Overall, 9.3% (95% CI, 4.7-13.9%) of the patients had their management beneficially altered by adding dual-tracer PET/CT to their conventional CT/MRI results. Dual-tracer PET/CT substantially outperforms single-tracer methods in detecting extrahepatic hepatocellular carcinoma metastases, evidencing its reliability and significant role in refining clinical management strategies based on robust diagnostic performance.

Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis.

We sought to determine the diagnostic accuracy of radiomics features in predicting HPV status in oropharyngeal squamous cell carcinoma (SCC) compared to routine paraclinical measures used in clinical practice. Twenty-six articles were included in the systematic review, and thirteen were used for the meta-analysis. The overall sensitivity of the included studies was 0.78, the overall specificity was 0.76, and the overall area under the ROC curve was 0.84. The diagnostic odds ratio (DOR) equaled 12 (8, 17). Subgroup analysis showed no significant difference between radiomics features extracted from CT or MR images. Overall, the studies were of low quality in regard to radiomics quality score, although most had a low risk of bias based on the QUADAS-2 tool. Radiomics features showed good overall sensitivity and specificity in determining HPV status in OPSCC, though the low quality of the included studies poses problems for generalizability.

Predicting survival of patients with advanced hepatocellular carcinoma receiving combination targeted immunotherapy: an evaluation of volumetric imaging parameters.

PURPOSE: To evaluate the potential of volumetric imaging in predicting survival of advanced hepatocellular carcinoma (HCC) patients receiving immunotherapy. METHODS: Retrospective analysis included 40 patients with advanced HCC who received targeted immunotherapy. Baseline and follow-up contrast-enhanced abdominal computed tomography (CT) scans were analyzed. The largest tumor was chosen as the index lesion. Viable tumor volume (qViable) and percentage tumor viability (%Viability) were calculated. Response Evaluation Criteria in Solid Tumors (RECIST) and Tumor volume change after treatment (qRECIST) were measured. Associations with overall survival (OS) were assessed. Cox regression analysis assessed the association between variables and overall survival (OS). A new prognostic stratification system was attempted to categorize patients based on significant predictors of OS. Patients with a baseline %viability > 69% and %viability reduction ≥ 8% were classified as better prognosis. Patients were stratified into better, intermediate and worse prognosis groups based on baseline %viability > 69% and ≥ 8% %viability reduction (better prognosis); baseline %viability ≤ 69% and < 8% %viability reduction (worse prognosis); remainder were intermediate prognosis. RESULTS: Patients with baseline %Viability > 69% and %Viability reduction ≥ 8% showed significantly higher OS. Multivariate analysis confirmed %Viability and %Viability reduction as significant predictors of OS. A prognostic stratification system using these parameters stratified patients into better, intermediate and worse prognosis groups, with the better prognosis showing highest OS. Most patients (97.5%) had stable disease by RECIST while the prognostic model re-classified 47.5% as better prognosis, 37.5% intermediate prognosis, and 15% worse prognosis. CONCLUSION: Volumetric parameters of %Viability and %Viability reduction predict OS in HCC patients undergoing immunotherapy.

Utilization of texture features of volumetric ADC maps in differentiating between serous cystadenoma and intraductal papillary neoplasms.

INTRODUCTION: The rising incidence of incidental detection of pancreatic cystic neoplasms has compelled radiologists to determine new diagnostic methods for the differentiation of various kinds of lesions. We aim to demonstrate the utility of texture features extracted from ADC maps in differentiating intraductal papillary mucinous neoplasms (IPMN) from serous cystadenomas (SCA). METHODS: This retrospective study was performed on 136 patients (IPMN = 87, SCA = 49) split into testing and training datasets. A total of 851 radiomics features were extracted from volumetric contours drawn by an expert radiologist on ADC maps of the lesions. LASSO regression analysis was used to determine the most predictive set of features and a radiomics score was developed based on their respective coefficients. A hyper-optimized support vector machine was then utilized to classify the lesions based on their radiomics score. RESULTS: A total of four Wavelet features (LHL/GLCM/LCM2, HLL/GLCM/LCM2, /LLL/First Order/90percent, /LLL/GLCM/MCC) were selected from all of the features to be included in our classifier. The classifier was optimized by altering hyperparameters and the trained model was applied to the validation dataset. The model achieved a sensitivity of 92.8, specificity of 90%, and an AUC of 0.97 in the training data set, and a sensitivity of 83.3%, specificity of 66.7%, and AUC of 0.90 in the testing dataset. CONCLUSION: A support vector machine model trained and validated on volumetric texture features extracted from ADC maps showed the possible beneficence of these features in differentiating IPMNs from SCAs. These results are in line with previous regarding the role of ADC maps in classifying cystic lesions and offers new evidence regarding the role of texture features in differentiation of potentially neoplastic and benign lesions.

Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study.

INTRODUCTION: Transarterial radioembolization (TARE) is one of the most promising therapeutic options for hepatic masses. Radiomics features, which are quantitative numeric features extracted from medical images, are considered to have potential in predicting treatment response in TARE. This article aims to provide meta-analytic evidence and critically appraise the methodology of radiomics studies published in this regard. METHODS: A systematic search was performed on PubMed, Scopus, Embase, and Web of Science. All relevant articles were retrieved, and the characteristics of the studies were extracted. The Radiomics Quality Score and Checklist for Evaluation of Radiomics Research were used to assess the methodologic quality of the studies. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve in predicting objective response were determined. RESULTS: The systematic review included 15 studies. The average Radiomics Quality Score of these studies was 11.4 ± 2.1, and the average Checklist for Evaluation of Radiomics Research score was 33± 6.7. There was a notable correlation (correlation coefficient = 0.73) between the two metrics. Adherence to quality measures differed considerably among the studies and even within different components of the same studies. The pooled sensitivity and specificity of the radiomics models in predicting complete or partial response were 83.5% (95% confidence interval 76%-88.9%) and 86.7% (95% confidence interval 78%-92%), respectively. CONCLUSION: Radiomics models show great potential in predicting treatment response in TARE of hepatic lesions. However, the heterogeneity seen between the methodologic quality of studies may limit the generalizability of the results. Future initiatives should aim to develop radiomics signatures using multiple external datasets and adhere to quality measures in radiomics methodology.

Patient Characteristics Associated With Embolization Versus Hysterectomy for Uterine Fibroids: A Systematic Review and Meta-Analysis.

INTRODUCTION: Black and underinsured women in the United States are more likely than their counterparts to develop uterine fibroids (UFs) and experience more severe symptoms. Uterine artery embolization (UAE), a uterine-sparing therapeutic procedure, is less invasive than the common alternative, open hysterectomy. To determine whether demographic disparities persist in UF treatment utilization, we reviewed patient characteristics associated with UAE versus hysterectomy for UF among studies of US clinical practices. METHODS: A systematic literature review was conducted via PubMed, Embase, and CINAHL (PROSPERO CRD42023455051), yielding 1,350 articles (January 1, 1995, to July 15, 2023) that outlined demographic characteristics of UAE compared with hysterectomy. Two readers screened for inclusion criteria, yielding 13 full-text US-based comparative studies specifying at least one common demographic characteristic. Random effects meta-analysis was performed on the data (STATA v18.0). Egger's regression test was used to quantify publication bias. RESULTS: Nine (138,960 patients), four (183,643 patients), and seven (312,270 patients) studies were analyzed for race, insurance status, and age as predictors of treatment modality, respectively. Black race (odds ratio = 3.35, P < .01) and young age (P < .05) were associated with UAE, whereas private insurance (relative to Medicare and/or Medicaid) was not (odds ratio = 1.06, P = .52). Between-study heterogeneity (I2 > 50%) was detected in all three meta-analyses. Small-study bias was detected for age but not race or insurance. CONCLUSIONS AND IMPLICATIONS: Knowledge of demographic characteristics of patients with UFs receiving UAE versus hysterectomy is sparse (n = 13 studies). Among these studies, which seem to be racially well distributed, Black and younger women are more likely to receive UAE than their counterparts.

Utilization of Radiomics Features Extracted From Preoperative Medical Images to Detect Metastatic Lymph Nodes in Cholangiocarcinoma and Gallbladder Cancer Patients: A Systemic Review and Meta-analysis.

OBJECTIVES: This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer. METHODS: Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability. RESULTS: Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively). CONCLUSIONS: The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.

Multimodality imaging of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Hepatocellular carcinoma and intrahepatic cholangiocarcinoma are the two most common primary malignant tumors of the liver. The similarities and variations in imaging characteristics that may aid in distinguishing between these two primary tumors will be discussed and outlined in this review. Knowledge of imaging techniques that are currently available would assist in the differentiation between these primary malignancies.

Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective.

The latest developments in cancer immunotherapy, namely the introduction of immune checkpoint inhibitors, have led to a fundamental change in advanced cancer treatments. Imaging is crucial to identify tumor response accurately and delineate prognosis in immunotherapy-treated patients. Simultaneously, advances in image acquisition techniques, notably functional and molecular imaging, have facilitated more accurate pretreatment evaluation, assessment of response to therapy, and monitoring for tumor recurrence. Traditional approaches to assessing tumor progression, such as RECIST, rely on changes in tumor size, while new strategies for evaluating tumor response to therapy, such as the mRECIST and the EASL, rely on tumor enhancement. Moreover, the assessment of tumor volume, enhancement, cellularity, and perfusion are some novel techniques that have been investigated. Validation of these novel approaches should rely on comparing their results with those of standard evaluation methods (EASL, mRECIST) while considering the ultimate outcome, which is patient survival. More recently, immunotherapy has been used in the management of primary liver tumors. However, little is known about its efficacy. This article reviews imaging modalities and techniques for assessing tumor response and survival in immunotherapy-treated patients with primary hepatic malignancies.

Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis.

Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.

The Additive Value of Radiomics Features Extracted from Baseline MR Images to the Barcelona Clinic Liver Cancer (BCLC) Staging System in Predicting Transplant-Free Survival in Patients with Hepatocellular Carcinoma: A Single-Center Retrospective Analysis.

BACKGROUND: To study the additive value of radiomics features to the BCLC staging system in clustering HCC patients. METHODS: A total of 266 patients with HCC were included in this retrospective study. All patients had undergone baseline MR imaging, and 95 radiomics features were extracted from 3D segmentations representative of lesions on the venous phase and apparent diffusion coefficient maps. A random forest algorithm was utilized to extract the most relevant features to transplant-free survival. The selected features were used alongside BCLC staging to construct Kaplan-Meier curves. RESULTS: Out of 95 extracted features, the three most relevant features were incorporated into random forest classifiers. The Integrated Brier score of the prediction error curve was 0.135, 0.072, and 0.048 for the BCLC, radiomics, and combined models, respectively. The mean area under the receiver operating curve (ROC curve) over time for the three models was 81.1%, 77.3%, and 56.2% for the combined radiomics and BCLC models, respectively. CONCLUSIONS: Radiomics features outperformed the BCLC staging system in determining prognosis in HCC patients. The addition of a radiomics classifier increased the classification capability of the BCLC model. Texture analysis features could be considered as possible biomarkers in predicting transplant-free survival in HCC patients.

An open-access breast lesion ultrasound image database : Applicable in artificial intelligence studies.

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS: An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS: Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION: A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.

Diffuse infiltrative hepatocellular carcinoma: Multimodality imaging manifestations.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, being the third most common cause of cancer-related death globally. HCC most frequently develops in the context of hepatic cirrhosis. HCC can manifest as various morphologic subtypes. Each pattern exhibits distinct behaviors in terms of imaging features, disease progression, response to therapy, and prognosis. While the nodular pattern is the most frequent subtype, infiltrative HCC is the least prevalent and makes up about 8%-20% of all HCC cases. Infiltrative HCC manifests as small tumor nodules that often spread across the entire liver or across a hepatic segment/lobe and is not identified as a focal tumor. On ultrasonography, infiltrative HCC presents as a markedly heterogeneous area with ill-defined echotexture, making it difficult to distinguish from background hepatic cirrhosis. On magnetic resonance imaging (MRI), infiltrating HCC typically manifests as a mild, poorly defined hepatic region with heterogeneous or homogenous aberrant signal intensity. Specifically, on T1-weighted MRI scans, infiltrating HCC frequently appears as largely hypointense and typically homogenous and mildly to moderately hyperintense on T2-weighted imaging. Infiltrative HCC frequently lacks a clearly defined boundary on cross-sectional imaging and can consequently fade into the background of the cirrhotic liver. As a result, infiltrating HCC is frequently not discovered until an advanced stage and has an associated poor prognosis. Thus, understanding imaging features associated with infiltrative HCC diagnosis is crucial for abdominal radiologists to ensure effective and timely care. We herein review imaging characteristics of infiltrative HCC.

Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts.

PURPOSE: To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images. METHOD: In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) were utilized for MLmodel development and external validation. The model was trained on ultrasound images of 725 breast lesions, and validation was done separately on the remaining data. An expert radiologist and a radiology resident classified the lesions based on the BI-RADS lexicon. Thirteen morphometric features were selected from a contour of the lesion and underwent a three-step feature selection process. Five features were chosen to be fed into the model separately and combined with the imaging signs mentioned in the BI-RADS reference guide. A support vector classifier was trained and optimized. RESULTS: The diagnostic profile of the model with various input data was compared to the expert radiologist and radiology resident. The agreement of each approach with histopathologic specimens was also determined. Based on BI-RADS and morphometric features, the model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.885, which is higher than the expert radiologist and radiology resident performances with AUC of 0.814 and 0.632, respectively in all cohorts. DeLong's test also showed that the AUC of the ML protocol was significantly different from that of the expert radiologist (ΔAUCs = 0.071, 95%CI: (0.056, 0.086), P = 0.005). CONCLUSIONS: These results support the possible role of morphometric features in enhancing the already well-excepted classification schemes.

Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?

OBJECTIVES: The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentiating benign and malignant lesions using image quantification and machine learning. METHODS: A total of 229 breast lesions and 220 cervical lymph nodes were included in the study. A group of expert radiologists first performed medical imaging and segmented the lesions, after which a rectangular mask was drawn, encompassing all of the contouring. The mask was extended in each axis up to 50%, and 29 radiomics features were extracted from each mask. Radiomics features that showed a significant difference in each contour were used to develop a support vector machine (SVM) classifier for benign and malignant lesions in breast and lymph node images separately. RESULTS: Single radiomics features extracted from extended contours outperformed radiologists' contours in both breast and lymph node lesions. Furthermore, when fed into the SVM model, the extended models also outperformed the radiologist's contour, achieving an area under the receiver operating characteristic curve of 0.887 and 0.970 in differentiating breast and lymph node lesions, respectively. CONCLUSIONS: Our results provide convincing evidence regarding the importance of the tumor periphery and TME in medical imaging diagnosis. We propose that the immediate tumor periphery should be considered for differentiating benign and malignant lesions in image quantification studies.

Radiofrequency ablation of parathyroid adenomas causing primary hyperparathyroidism: A report of 27 patients.

PURPOSE: To study treatment outcome of parathyroid adenomas using ultrasound-guided radiofrequency ablation. METHODS: Twenty-seven patients with a single adenoma of the parathyroid gland were included in the study. Using color Doppler ultrasonography, the lesion and its characteristics were determined, and dextrose was injected to dissect the gland from the surrounding structures. The ablation process was done with 6-12 watts of power. RESULTS: No complications were seen in any of the subjects. A significant reduction was seen in serum parathyroid hormone (PTH) and calcium levels after treatment. PTH levels showed a median decrease of 13.8%, and a median decrease of 8.2% was seen in serum calcium levels (p < 0.001). Phosphorus levels did not change significantly after treatment. In 1-month follow-up of patients, the lesion size had decreased considerably. In long-term follow-up, 11 of 20 patients having subsequent imaging had indistinguishable lesions. CONCLUSION: Our results showed that RFA of parathyroid adenomas caused a significant reduction in biomedical indicators of disease and resulted in a significant reduction or disappearance of the lesion in the majority of the patients while having no considerable complications.

Nanotechnology-based advances in the efficient delivery of melatonin.

N-[2-(5-methoxy-1H-indol-3-yl) ethyl] or simply melatonin is a biogenic amine produced by pineal gland and recently recognized various other organs. Because of a broad range of biological function melatonin is considered as a therapeutic agent with high efficacy in the treatment of multiple disorders, such as cancer, degenerative disorders and immune disease. However, since melatonin can affect receptors on the cellular membrane, in the nucleus and can act as an anti-oxidant molecule, some unwanted effects may be observed after administration. Therefore, the entrapment of melatonin in biocompatible, biodegradable and safe nano-delivery systems can prevent its degradation in circulation; decrease its toxicity with increased half-life, enhanced pharmacokinetic profile leading to improved patient compliance. Because of this, nanoparticles have been used to deliver melatonin in multiple studies, and the present article aims to cumulatively illustrate their findings.

Age at Diagnosis of Breast Cancer in Iran: A Systematic Review and Meta-Analysis.

BACKGROUND: Breast cancer is responsible for up to 25% of all cancers in Iran. The age at diagnosis of Iranian breast cancer patients starts a decade earlier than most of developed countries. This study aimed to evaluate the mean age at diagnosis of Iranian breast cancer patients. METHODS: In this systematic review and meta-analysis, the mean age at diagnosis of Iranian breast cancer patients and its pattern between 2008 and 2017, were evaluated. All papers with age at diagnosis of histopathological verified breast cancer patients were considered eligible to enter to the analysis. We used databases including Medline/PubMed, Scopus, Embase, Cochrane Library, Iranmedex and SID for the search process. The meta-analysis was performed only on studies with separate data for female patients, using random-effects model, Mantel and Haenszel method and the Comprehensive Meta-analysis software. RESULTS: Finally, 92 studies with 19,784 patients (both-genders) were included. The mean age at diagnosis had increased from 47.93 (2008) to 49.91 (2016) years. The meta-analysis was done on 78 studies containing of 15,071 female patients and the mean age at diagnosis was 46.76±1.19. There was a wide range of age at diagnosis within different provinces. The mean age at Hamadan and Khuzestan provinces were the lowest and highest, respectively (42.48±7.96 vs. 51.00±11.47). The heterogeneity of studies was statistically significant (I2=99.744). CONCLUSION: Mean age at diagnosis of Iranian women with breast cancer was 46.76±1.19. There was an increasing pattern in mean age of diagnosis at breast cancer patients within the past 10 years.