Predict Tumor Histology Research Articles

Literature Review on Effective Use of Modern Modalities in Management of Intra-Axial Brain Lesions

The significant health burden caused by intra-axial brain lesions and the difficulties associated with their diagnosis and treatment have prompted a large number of researchers and practitioners to investigate various diagnostic and treatment modalities for their effectiveness and safety. This study focuses on evaluating different pre-, intra- and postoperative techniques and analyzes their advantages and limitations to ultimately improve the management of this type of brain lesion. Through a critical analysis of various scientific sources, this research aimed to synthesize existing knowledge on the topic. Magnetic resonance imaging plays a crucial role in diagnosing and managing brain tumors. Functional MRI identifies functional brain areas, but tumor-induced blood flow changes can affect its reliability. Connectome analysis provides information on functional localization and brain networks, which became possible using diffusion tensor imaging, that visualizes white matter pathways, aiding in tumor boundary delineation and surgical planning. Studies suggest this method can predict tumor histology and prognosis. Intraoperative MRI improves the extent of tumor resection and potentially patient survival. But due to its limitations it has alternative intraoperative techniques, like intraoperative ultrasound, fluorescence-guided surgery, direct electrical stimulation, deep brain stimulation, but evidence for most of these methods is limited for most brain tumors. Only ultrasound showed real-time tumor visualization and residual disease analysis offering high accuracy and is relatively inexpensive compared to ioMRI. This study can be useful to neurosurgeons, neuro-oncologists, neurologists, neuro-radiologists, and will demonstrate prospects for further research.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches.

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

Machine Learning Model as a Useful Tool for Prediction of Thyroid Nodules Histology, Aggressiveness and Treatment-Related Complications.

Thyroid nodules are very common, 5-15% of which are malignant. Despite the low mortality rate of well-differentiated thyroid cancer, some variants may behave aggressively, making nodule differentiation mandatory. Ultrasound and fine-needle aspiration biopsy are simple, safe, cost-effective and accurate diagnostic tools, but have some potential limits. Recently, machine learning (ML) approaches have been successfully applied to healthcare datasets to predict the outcomes of surgical procedures. The aim of this work is the application of ML to predict tumor histology (HIS), aggressiveness and post-surgical complications in thyroid patients. This retrospective study was conducted at the ENT Division of Eastern Piedmont University, Novara (Italy), and reported data about 1218 patients who underwent surgery between January 2006 and December 2018. For each patient, general information, HIS and outcomes are reported. For each prediction task, we trained ML models on pre-surgery features alone as well as on both pre- and post-surgery data. The ML pipeline included data cleaning, oversampling to deal with unbalanced datasets and exploration of hyper-parameter space for random forest models, testing their stability and ranking feature importance. The main results are (i) the construction of a rich, hand-curated, open dataset including pre- and post-surgery features (ii) the development of accurate yet explainable ML models. Results highlight pre-screening as the most important feature to predict HIS and aggressiveness, and that, in our population, having an out-of-range (Low) fT3 dosage at pre-operative examination is strongly associated with a higher aggressiveness of the disease. Our work shows how ML models can find patterns in thyroid patient data and could support clinicians to refine diagnostic tools and improve their accuracy.

Preoperative Diffusion Tensor Imaging in Supratentorial Intra-Axial Brain Tumors: Its Role in Predicting Tumor Histology and Prognosis as well in Surgical Planning and Resection.

Objective There are a large number of prospective studies that use diffusion tensor imaging (DTI) to show the relationship between intracranial tumors and white matter (WM) fibers. We studied the role of DTI in supratentorial intra-axial (ST-IA) tumors of the brain in deciding the surgical approach with maximal resection and minimal or no deficit and in predicting the histological characterization of the tumor and the neurological outcome. Methods A total of 91 cases of ST-IA tumors were included in our study. The neurological status of the patients was assessed preoperatively, and the tumor volume and DTI pattern were noted radiologically. Surgical plan was decided by the senior consultants of the neurosurgery department taking into consideration the findings of tractography and magnetic resonance imaging. The neurological status and the extent of resection were evaluated postoperatively, and the correlation between histopathology with DTI was studied. Results Of the 91 patients, 25 had high-grade glioma (HGG), 60 had low-grade glioma (LGG), and 6 were metastatic lesions. Gross total excisions were done mostly in patients with DTI showing displaced fibers and subtotal/partial resections were done mostly in disrupted/infiltrated tracts, which was statistically significant. The correlation between histopathology and tractography revealed that intact/displaced tracts were seen mostly in LGG (79%), whereas 86% of HGG showed disrupted/infiltrated fibers; both were statistically significant. Conclusion Preoperative DTI in ST-IA brain tumors is an important tool for deciding the appropriate surgical approach for maximal safe resection, thus improving the post-op neurological outcome in patients. It also helps in predicting the tumor histology while also serving as an important prognostication indicator.

Scope of Artificial Intelligence in Gastrointestinal Oncology.

Simple SummaryGastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital tool for the management of these cancer patients.Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC=0.72±0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC=0.72±0.04 for Random Forest and AUC=0.84±0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study.

to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.

MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (MP61)1 Sep 2021MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY Rishi Sekar, Giuseppe Toia, Achille Mileto, and George Schade Rishi SekarRishi Sekar More articles by this author , Giuseppe ToiaGiuseppe Toia More articles by this author , Achille MiletoAchille Mileto More articles by this author , and George SchadeGeorge Schade More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002101.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Dual-Energy Computed Tomography (DECT) has been shown to more accurately characterize renal mass vascularity, however its ability to differentiate tumor subtypes remains unclear. Herein, we investigate the role of DECT and iodine concentration as a derived imaging biomarker in predicting tumor histology and Fuhrman Nuclear Grade (FNG). METHODS: Our institutional database was queried for patients who underwent contrast enhanced DECT to characterize renal masses with subsequent pathologic diagnosis within our institution from 2018 to present. Tumor histology and FNG were obtained from internal pathologic evaluation. Tumor enhancement was measured in Hounsfield Units (HU) using standard techniques. Tumor iodine concentration (mg/mL) was calculated from the maximally enhancing region within each tumor in the nephrogenic phase. Mean enhancement (ME) and mean iodine concentration (MIC) were stratified by histology, and across FNG for clear cell renal cell carcinoma (ccRCC) and compared between groups using Student’s t-test. RESULTS: Our study cohort consisted of 39 separate renal solid enhancing masses. Diagnosis was ccRCC in 22 (56.4%), non-ccRCC in 12 (30.8%), and oncocytoma in 5 (12.8%). Among RCCs, both ME and MIC were statistically significantly higher for ccRCCs versus non-ccRCCs (ME: 116.0 vs. 65.9 HU, p=0.02; MIC: 5.5 vs 3.8 mg/mL, p=0.02).Among ccRCCs, ME was lower for high grade (FNG 3 and 4) versus low grade (FNG 1 and 2) tumors (86.2 vs. 145.9 HU, p=0.1), while MIC was higher (5.7 vs 5.4 mg/mL, p=0.7), however these relationships did not reach statistical significance. Further, ME was higher for chromophobe RCC versus oncocytoma (115 vs. 104.4 HU), while MIC was lower (5.3 vs 6.7 mg/mL, no statistical analysis performed due to small sample size). CONCLUSIONS: DECT imaging with tumor MIC may improve the diagnostic ability to differentiate between renal mass histologies compared to standard contrast enhanced CT imaging. Our preliminary data shows a trend towards higher iodine concentration in high grade ccRCCs compared to low grade tumors and benign histologies. Further investigation with larger cohorts is needed to delineate diagnostic thresholds and allow correlation with oncologic outcomes that could lead to improved prognostic tools. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1083-e1083 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rishi Sekar More articles by this author Giuseppe Toia More articles by this author Achille Mileto More articles by this author George Schade More articles by this author Expand All Advertisement Loading ...

Deep learning classification of lung cancer histology using CT images

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.

Characterization of high-grade pineal region lesions: the usefulness of apparent diffusion coefficient volumetric values.

High-grade pineal region tumors are rare and heterogeneous types of primary central nervous system neoplasms; radiological differential diagnosis is challenging but it is important because it has a therapeutic relevance. To discriminate among high-grade pineal region tumors by combining apparent diffusion coefficient (ADC) volumetric values and qualitative features in order to predict their histology. Twenty-two patients with high-grade pineal region tumors were assessed by qualitative and quantitative analysis. Margins, T2-weighted signal intensity, contrast enhancement, hemorrhage, calcifications, different volumetric ADC fractions (ADCmean, ADCmax, ADCmin) were evaluated and were compared to the histopathologic findings (cell count and proliferation index). Our qualitative imaging data showed that only margins were different among different tumors and each tumor type showed peculiar age onset. ADCmean was found the best quantitative value to discriminate high-grade tumors of the pineal region. ADCmean correlated with proliferation index but not with cell count. ADCmean values were lower in tumors with higher proliferation rate and a significant difference in ADCmean values were found between germinomas and pineoblastomas, between germinomas and papillary tumors and between papillary tumors and pineoblastomas. Moreover, the cut-off value of 0.865 × 10-3 mm2/s for ADCmean (ADC mean threshold value) could differentiate germinoma from pineoblastomas with the best combination of sensitivity and specificity. The ADCmean value measured on the whole tumor, reflecting tumor proliferative activity, may be a practical and non-invasive marker for predicting tumor histology in high-grade pineal region lesions and might be useful in preoperative assessment.

Optimal Approach to Small Unilateral Testicular Nodules-Can We Reliably Predict Tumor Histology Based on Size?

We evaluated primary testicular tumor characteristics associated with nongerm cell tumor histology and potential appropriateness for testis sparing surgery in selected patients from our institution. We retrospectively reviewed medical records of patients undergoing surgery for testicular masses between 2003 and 2015. We included patients with unilateral testicular tumors, normal preoperative serum tumor markers and no preoperative evidence of metastatic spread. Demographic and clinical information were extracted. The primary outcome studied was tumor pathology, germ cell tumor histology vs nongerm cell histology. We compared patients in these cohorts based on the testicular tumor size. A total of 48 patients met study criteria, 18 (37.5%) of whom had a final pathology consistent with nongerm cell histology. In general, the median tumor size was less in the nongerm cell group (11 mm vs 27 mm, p=0.001). Tumor size less than 2 cm was associated with increased likelihood of nongerm cell histology (p=0.003) with 61.9% of those with tumors less than 2 cm harboring nongerm cell tumors and therefore likely appropriate for organ-sparing surgery. A receiver operating characteristic analysis demonstrated a maximum sensitivity and specificity for selecting masses with normal tumor markers as having nongerm cell histology at a size cutoff of 18 mm. It appears that a majority of patients with localized small testicular masses and nonelevated tumor markers will have nongerm cell histology, which makes them potentially eligible for testicular sparing surgery at centers with expertise in intraoperative frozen section analysis.

ROLE OF R.E.N.A.L NEPHROMETRY IN PREDICTING TUMOR HISTOLOGY AND GRADE OF RENAL MASS.

Background: The R.E.N.A.L.(radius, exophytic/endophytic properties, nearness of tumor to the collecting systemor sinus in mm, anterior/posterior location relative to polar lines) nephrometry scoring system was recently introduced as an objective reproducible means to describe salient renal tumor anatomy. Objective of this study is to evaluate the role of R.E.N.A.L. Nephrometry score in predicting tumor histology and grade.
 Methods: It is a prospective study carried out in Lilawati Hospital and Research Centre, a tertiary care centre consisting of 40 patients including male and female who had T1 renal mass. Preoperative R.E.N.A.L. Nephrometry scoring done for every patient and after treatment (Open Partial Nephrectomy, Laproscopic partial Nephrectomy, Radical Nephrectomy) tumor sent for histopathology to predict tumor histology and grade for solid renal mass.
 Results: Clear cell histology also increased with R.E.N.A.L score, from 2/6 (33.3%) in patients with low R.E.N.A.L scores ( 4–6) up to 15/17 (88.24%) for patients with high R.E.N.A.L scores( 10–12) . Conversely, the probability of the potentially more indolent papillary RCC decreased with increasing lesion complexity (from 66.67% in low score to 11.76% in moderate score).The Fuhrman grade is an important prognostic indicator for RCCs. In our study Fuhrman grade 1 tumours represented 4/40 (66.7%) low and 2(13%)high- complexity lesions . Conversely, there is no grade 3 lesion in low score (0.0%) compared with 5/40 (33.3%%) moderate complexity lesions, respectively showing more the nephrometry score higher will be Fuhrman grade.
 Conclusions: Proportion with clear cell histology also increases with R.E.N.A.L score and the probability of the potentially more indolent papillary RCC decreased with increasing lesion complexity. The Fuhrman grade also increases with increase in nephrometry score
 Keywords: R.E.N.A.L Nephrometry score, Fuhrman grade, Tumor histology

Utility of Viscoelastic Assays Beyond Coagulation: Can Preoperative Thrombelastography Indices Predict Tumor Histology, Nodal Disease, and Resectability in Patients Undergoing Pancreatectomy?

Hypercoagulability and malignancy have been linked since the 1860s. However, the impact of different neoplasms on multiple components of the coagulation system remains poorly understood. Thrombelastography (TEG) enables measurement of coagulation incorporating clotting through fibrinolysis. We hypothesize that specific TEG indices that are associated with hypercoagulability can be appreciated in patients with adenocarcinoma undergoing pancreatic resection. Blood samples were obtained from patients undergoing pancreatic resection before surgical incision and assayed with TEG. The 4 indices of coagulation measured by TEG included in the analysis were R time, angle, maximum amplitude, and lysis at 30 minutes. Patient tumor type, nodal disease, and mass resectability were contrasted with TEG indices. One hundred patients were enrolled over 18 months. The majority (63%) of patients had adenocarcinoma. Patients with adenocarcinoma had increased angle compared with other lesions (49 degrees [interquartile range {IQR} 37 to 59 degrees] vs 43 degrees [IQR 32 to 49 degrees]; p= 0.011). When excluding patients that underwent neoadjuvant therapy, patients with adenocarcinoma had shorter R times (13 minutes [IQR 9 to 16 minutes] vs 14 minutes [IQR 12 to 18 minutes]; p= 0.051), steeper angles (49 degrees [IQR 40 to 59 degrees] vs 43 degrees [IQR 32 to 49 degrees]; p= 0.010), and higher maximum amplitude (67 mm [IQR 61 to 69 mm] vs 62 mm [IQR 57 to 67 mm]; p= 0.017). Nodal disease was associated with a significantly increased angle (49 degrees [IQR 42 to 59 degrees] vs 40 degrees [IQR 32 to 50 degrees]; p= 0.002) and maximum amplitude (64 mm [IQR 61 to 69 mm] vs 62 mm [IQR 56 to 67 mm]; p= 0.017). Patients who underwent successful mass resection had longer R times (14 minutes [IQR 11 to 17 minutes] vs 10 minutes [IQR 9 to 15]; p=0.033) and shorter angles (44 degrees [IQR 35 to 55 degrees] vs 58 degrees [IQR 45 to 66 degrees]; p= 0.025). Patients with adenocarcinoma undergoing pancreatic resection have multiple TEG abnormalities consistent with hypercoagulability. These TEG outputs are associated with tumor type, nodal disease, and probability of a successful resection. The use of preoperative TEG has the potential to aid surgeon and patient discussions on anticipated disease burden and prognosis before resection.

Dynamic contrast-enhanced MRI perfusion for differentiating between melanoma and lung cancer brain metastases.

Brain metastases originating from different primary sites overlap in appearance and are difficult to differentiate with conventional MRI. Dynamic contrast‐enhanced (DCE)‐MRI can assess tumor microvasculature and has demonstrated utility in characterizing primary brain tumors. Our aim was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K trans) derived from DCE‐MRI in distinguishing between melanoma and nonsmall cell lung cancer (NSCLC) brain metastases. Forty‐seven NSCLC and 23 melanoma brain metastases were retrospectively assessed with DCE‐MRI. Regions of interest were manually drawn around the metastases to calculate Vpmean and Kmeantrans. The Mann–Whitney U test and receiver operating characteristic analysis (ROC) were performed to compare perfusion parameters between the two groups. The Vpmean of melanoma brain metastases (4.35, standard deviation [SD] = 1.31) was significantly higher (P = 0.03) than Vpmean of NSCLC brain metastases (2.27, SD = 0.96). The Kmeantrans values were higher in melanoma brain metastases, but the difference between the two groups was not significant (P = 0.12). Based on ROC analysis, a cut‐off value of 3.02 for Vpmean (area under curve = 0.659 with SD = 0.074) distinguished between melanoma brain metastases and NSCLC brain metastases (P < 0.01) with 72% specificity. Our data show the DCE‐MRI parameter Vpmean can differentiate between melanoma and NSCLC brain metastases. The ability to noninvasively predict tumor histology of brain metastases in patients with multiple malignancies can have important clinical implications.

Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size?

Ground-glass opacity (GGO) is reported to be characteristic to lepidic growth of neoplasm in subsolid nodules. In solid nodules of lung cancer, however, there is no characteristic feature to be reported. To study if there are any thin-section CT findings characteristic to tumor histology or if they are only related to tumor size in solid nodules of the lung cancer. This study included 106 solid peripheral lung cancers of 3 cm or smaller (56 adenocarcinomas, 33 squamous cell carcinomas, and 17 small cell carcinomas) in which 16-slice CT with 1 mm collimation was performed before surgery. Six morphologic findings (presence or absence of lobulation, coarse spiculation, air bronchogram, cavity, pleural tag, and pleural-based lesion) and four measurements (ratio of the greatest transverse and vertical diameter to the shortest transverse diameter and density of lobulation and coarse spiculation) on thin-section CT images were evaluated. Density of lobulation (coarse spiculation) was defined as the ratio of lobulation (coarse spiculation) number to the greatest transverse diameter of a nodule. Air bronchogram (P < 0.01) was the only significant factor for predicting lung adenocarcinoma. The prevalence of air bronchogram was significantly greater in adenocarcinoma than in squamous cell carcinoma (P < 0.01) or small cell carcinoma (P < 0.01). As the tumor size advanced, significantly positive linear trends were seen in the prevalence of lobulation (P < 0.01), coarse spiculation (P < 0.01), and pleural tag (P < 0.01), and the mean values of density of lobulation (P < 0.01) and coarse spiculation (P < 0.01), while the significant negative linear trend was seen in the ratio of vertical diameter to the shortest transverse (P = 0.02). Air bronchogram on thin-section CT is characteristic feature of solid adenocarcinoma of the lung. However, other thin-section CT findings are irrelevant to tumor histology and related only to tumor size.

Evaluation of Apparent Diffusion Coefficient Thresholds for Diagnosis of Medulloblastoma Using Diffusion-Weighted Imaging

We assess a diffusion-weighted imaging (DWI) analysis technique as a potential basis for computer-aided diagnosis (CAD) of pediatric posterior fossa tumors. A retrospective medical record search identified 103 children (mean age: 87 months) with posterior fossa tumors having a total of 126 preoperative MR scans with DWI. The minimum ADC (ADCmin) and normalized ADC (nADC) values [ratio of ADCmin values in tumor compared to normal tissue] were measured by a single observer blinded to diagnosis. Receiver operating characteristic (ROC) curves were generated to determine the optimal threshold for which the nADC and ADCmin values would predict tumor histology. Inter-rater reliability for predicting tumor type was evaluated using values measured by two additional observers. At histology, ten tumor types were identified, with astrocytoma (n=50), medulloblastoma (n=33), and ependymoma (n=9) accounting for 89%. Mean ADCmin (0.54 × 10(-3) mm(2)/s) and nADC (0.70) were lowest for medulloblastoma. Mean ADCmin (1.28 × 10(-3) mm(2)/s) and nADC (1.64) were highest for astrocytoma. For the ROC analysis, the area under the curve when discriminating medulloblastoma from other tumors using nADC was 0.939 and 0.965 when using ADCmin. The optimal ADCmin threshold was 0.66 × 10(-3) mm(2)/s, which yielded an 86% positive predictive value, 97% negative predictive value, and 93% accuracy. Inter-observer variability was very low, with near perfect agreement among all observers in predicting medulloblastoma. Our data indicate that both ADCmin and nADC could serve as the basis for a CAD program to distinguish medulloblastoma from other posterior fossa tumors with a high degree of accuracy.

Functional MRI procedures in the diagnosis of brain tumors: Perfusion- and diffusion-weighted imaging

Despite the increased diagnostic accuracy of contrast material enhanced MR imaging, specification and grading of brain tumors are still only approximate at best: neither morphology, nor relaxation times or contrast material enhancement reliably predict tumor histology or tumor grade. As histology and tumor grade strongly influence which therapy concept is chosen, a more precise diagnosis is mandatory. With diffusion- and perfusion-weighted MR imaging (DWI, PWI) it is now possible to obtain important information regarding the cellular matrix and the relative regional cerebral blood volume (rrCBV) of brain tumors, which cannot be obtained with standard MR techniques. These dynamic-functional imaging techniques are very useful in the preoperative diagnosis of gliomas, lymphomas, and metastases, as well as in the differentiation of these neoplastic lesions from abscesses, atypical ischemic infarctions, and tumor-like manifestations of demyelinating disease. Additionally, they appear suitable for determining glioma grade and regions of active tumor growth which should be the target of stereotactic biopsy and therapy. After therapy these techniques are helpful to better assess the tumor response to therapy, possible therapy failure and therapy complications such as radiation necrosis.

Magnetic resonance imaging for solitary lesions of bone: when, why, how useful?

The radiologist and orthopedic surgeon have a wide array of imaging modalities to choose from in diagnosing and staging solitary tumors of bone. MR imaging is the examination of choice for staging solitary tumors of bone. The radiograph, however, remains the most reliable imaging modality for predicting tumor histology. Radiographic interpretation in most instances ought to be the most influential factor in determining whether further imaging is required. This article reviews the strengths and weaknesses of the various imaging modalities and attempts to present a logical, cohesive, reproducible approach to the diagnosis and evaluation of solitary tumors of bone.