Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

BackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.MethodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2–5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.ResultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.ConclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.

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Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis

Canadian Urological Association Journal ◽

10.5489/cuaj.5538 ◽

2018 ◽

Vol 13 (7) ◽

Cited By ~ 2

Author(s):

Mustafa Andkhoie ◽

Desneige Meyer ◽

Michael Szafron

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Principal Component Analysis ◽

Scoping Review ◽

Principal Component ◽

Treatment Decision ◽

Localized Prostate Cancer ◽

Key Factors ◽

Treatment Decision Making ◽

Word Cloud

Introduction: The purpose of this research is to gather, collate, and identify key factors commonly studied in localized prostate cancer (LPC) treatment decision-making in Canada and the U.S.Methods: This scoping review uses five databases (Medline, EMBASE, CINAHL, AMED, and PsycInfo) to identify relevant articles using a list of inclusion and exclusion criteria applied by two reviewers. A list of topics describing the themes of the articles was extracted and key factors were identified using principal component analysis (PCA). A word cloud of titles and abstracts of the relevant articles was created to identify complementary results to the PCA.Results: This review identified 77 relevant articles describing 32 topics related to LPC treatment decision-making. The PCA grouped these 32 topics into five key factors commonly studied in LPC treatment decision-making: 1) treatment type; 2) socioeconomic/demographic characteristics; 3) personal reasons for treatment choice; 4) psychology of treatment decision experience; and 5) level of involvement in the decision-making process. The word cloud identified common phrases that were complementary to the factors identified through the PCA.Conclusions: This research identifies several possible factors impacting LPC treatment decision-making. Further research needs to be completed to determine the impact that these factors have in the LPC treatment decision-making experience.

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AUTOMATION OF MR BRAIN IMAGE CLASSIFICATION FOR MALIGNANCY DETECTION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419400025 ◽

2019 ◽

Vol 19 (01) ◽

pp. 1940002 ◽

Cited By ~ 3

Author(s):

K. V. AHAMMED MUNEER ◽

K. PAUL JOSEPH

Keyword(s):

Brain Tumor ◽

Nearest Neighbor ◽

Statistical Significance ◽

Principal Component ◽

Feature Reduction ◽

Brain Atlas ◽

Significant Feature ◽

Support Vector ◽

Brain Images ◽

Mr Brain

Magnetic resonance imaging (MRI) plays an integral role among the advanced techniques for detecting a brain tumor. The early detection of brain tumor with proper automation algorithm results in assisting oncologists to make easy decisions for diagnostic purposes. This paper presents an automatic classification of MR brain images in normal and malignant conditions. The feature extraction is done with gray-level co-occurrence matrix, and we proposed a feature reduction technique based on statistical test which is preceded by principal component analysis (PCA). The main focus of the work is to establish the statistical significance of the features obtained after PCA, thereby selecting significant feature values for subsequent classification. For that, a [Formula: see text]-test is performed which yielded a [Formula: see text]-value of 0.05. Finally, a comparative study using [Formula: see text]-nearest neighbor (kNN), support vector machine and artificial neural network (ANN)-based supervised classifiers is performed. In this work, we could achieve reasonably good sensitivity, specificity and accuracy for all the classifiers. The ANN classifier gives better performance with sensitivity of 97.33%, specificity of 97.42% and accuracy of 98.66% on the whole brain atlas database. The experimental results obtained are comparable to the other recent state-of-the-art.

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Use of serum biomarkers in staging of canine hepatic fibrosis

Journal of Veterinary Diagnostic Investigation ◽

10.1177/1040638719866881 ◽

2019 ◽

Vol 31 (5) ◽

pp. 665-673 ◽

Cited By ~ 1

Author(s):

Maud Menard ◽

Alexis Lecoindre ◽

Jean-Luc Cadoré ◽

Michèle Chevallier ◽

Aurélie Pagnon ◽

...

Keyword(s):

Liver Biopsy ◽

Hepatic Fibrosis ◽

External Validation ◽

Model Performance ◽

Area Under The Curve ◽

Gamma Glutamyl Transferase ◽

Training Set ◽

Internal Validation ◽

Validation Set ◽

Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

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The predictive potential of the spatial signature of lymphocytes in breast cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.530 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 530-530

Author(s):

Nora Balint-Lahat ◽

Chen Mayer ◽

Noa Ben-Baruch ◽

Ady Yosepovich ◽

Kira Sacks ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Early Stage ◽

Principal Component ◽

Treatment Decision ◽

High Ratio ◽

Luminal Breast Cancer ◽

Breast Cancers ◽

Breast Cancer Patients ◽

Validation Set

530 Background: Tumor-infiltrating lymphocytes in breast cancer have emerged as both a prognostic and a potentially predictive immunotherapy biomarker. Advancements in artificial intelligence can extract pathology-based spatial immune fingerprints for use as treatment decision support tools. Methods: We examined 908 primary breast cancer patients with whole slide images (WSI) available from TCGA database. Digital structuring of WSIs included automated detection of lymphocytes, tumor and tumor adjacent stroma, using deep learning-based semantic segmentation. Prognosis was defined as progression free interval (PFI). A Cox Survival analysis was used to detect prognostic spatial features. We used principal component analysis (PCA) to reduce and decorrelate significant features. The resulting PCA features were used to fit the final model. The model was then validated on an independent database of WSI of breast lumpectomies, from two tertiary hospitals in Israel. Results: The analysis included 908 WSI. The average age was 58.4 years old, with a majority of early stage breast cancer (76.7%, stage I and II). The detection performance for tumor area and lymphocytes reached F1 scores of 99% and 97% respectively, in comparison to human annotation. In the Kaplan Meier (KM) analysis of 414 early stage luminal breast cancers, a high number of lymphocyte clusters (LC) and a high ratio between stromal lymphocyte density and tumor lymphocyte density (LD-S/LD-T) were significantly associated with longer PFI (p = 0.005 and p = 0.038, respectively). Based on these features, two continuous PCA features were added to the multivariate model, and remained significantly associated with PFI after adjusting for age (HR = 1.19, 95% CI 1.05-1.35; HR = 1.26 95% CI 1.03-1.55). The validation set was underpowered (n = 79) and data is still being collected. In a preliminary KM analysis of 37 early stage luminal breast cancer cases from the validation set, LD-S/LD-T was significantly associated with longer PFI (p = 0.046). Conclusions: In our study, LC and LD-S/LD-T, presumably surrogate measures of peritumoral lymphocytes, were found significantly associated with longer PFI.

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An adapted neural-fuzzy inference system model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in patients after heart valve replacement

10.21203/rs.3.rs-224969/v1 ◽

2021 ◽

Author(s):

Zhi-Chun Gu ◽

Shou-Rui Huang ◽

Dong Li ◽

Qin Zhou ◽

Jing Wang ◽

...

Keyword(s):

Fuzzy Inference ◽

Predictive Accuracy ◽

External Validation ◽

High Dose ◽

Internal Validation ◽

Inference System ◽

Dose Warfarin ◽

The Mean ◽

Balanced Model ◽

Validation Set

Abstract Background Tailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and huge inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). Methods This retrospective study enrolled patients who underwent HVR between June 1, 2012 and June 1, 2016 from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose, and the models’ predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general liner model and created two ANFIS models using imbalanced and balanced training sets. We finally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Stratified analyses were conducted across warfarin doses (low, medium, and high doses). Results A total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2,820 in the balanced training set; 1,511 in the internal validation set; and 1,511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage: 74.39–78.16%, MAE: 0.37 mg/daily, MSE: 0.39 mg/daily) and the balanced model (ideal prediction percentage: 73.46–75.31%, MAE: 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a significantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort confirmed this finding. Conclusions The ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low- and high-dose warfarin groups.

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Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Frontiers in Oncology ◽

10.3389/fonc.2021.775079 ◽

2021 ◽

Vol 11 ◽

Author(s):

Aihua Wu ◽

Zhigang Liang ◽

Songbo Yuan ◽

Shanshan Wang ◽

Weidong Peng ◽

...

Keyword(s):

Pleural Effusion ◽

Diagnostic Accuracy ◽

Prediction Model ◽

Scoring System ◽

Malignant Pleural Effusion ◽

Diagnostic Performance ◽

External Validation ◽

Training Set ◽

Internal Validation ◽

Validation Set

BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

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A Novel Radiomics-Platelet Nomogram for the Prediction of Gastroesophageal Varices Needing Treatment in Cirrhotic Patients

10.21203/rs.3.rs-186603/v1 ◽

2021 ◽

Author(s):

Yiken Lin ◽

Lijuan Li ◽

Dexin Yu ◽

Zhuyun Liu ◽

Shuhong Zhang ◽

...

Keyword(s):

External Validation ◽

Multivariate Logistic Regression Model ◽

Noninvasive Method ◽

Training Set ◽

Noninvasive Methods ◽

Internal Validation ◽

Gastroesophageal Varices ◽

Portal Phase ◽

Cirrhotic Patients ◽

Validation Set

Abstract Background and aimsHighly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis.MethodsIn this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad-score) was constructed with the least absolute shrinkage and selection operator algorithm and 10-folds cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. ResultsThe rad-score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the rad-score with platelet counts, achieving an AUC of 0.987(95% CI, 0.969-1.00), 0.973(95% CI, 0.939-1.00) and 0.947(95% CI, 0.876-1.00) in the training set, internal validation set and external validation set respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (<5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed.ConclusionsIn this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients.

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Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices—A Feasibility Study

Diagnostics ◽

10.3390/diagnostics11091676 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1676

Author(s):

Philipp Sager ◽

Lukas Näf ◽

Erwin Vu ◽

Tim Fischer ◽

Paul M. Putora ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

External Validation ◽

Tumor Detection ◽

Tumor Location ◽

Vestibular Schwannomas ◽

Internal Validation ◽

Contrast Enhanced ◽

The Difference ◽

Validation Set

Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869–0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702–0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks.

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An Immune-Clinical Prognostic Index (ICPI) for Patients With De Novo Follicular Lymphoma Treated With R-CHOP/CHOP Chemotherapy

Frontiers in Oncology ◽

10.3389/fonc.2021.708784 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yaxiao Lu ◽

Jingwei Yu ◽

Wenchen Gong ◽

Liping Su ◽

Xiuhua Sun ◽

...

Keyword(s):

De Novo ◽

External Validation ◽

Prognostic Index ◽

Risk Groups ◽

Chop Chemotherapy ◽

Training Set ◽

Chi Square ◽

T Lymphocyte Subsets ◽

Internal Validation ◽

Validation Set

PurposeAlthough the role of tumor-infiltrating T cells in follicular lymphoma (FL) has been reported previously, the prognostic value of peripheral blood T lymphocyte subsets has not been systematically assessed. Thus, we aim to incorporate T-cell subsets with clinical features to develop a predictive model of clinical outcome.MethodsWe retrospectively screened a total of 1,008 patients, including 252 newly diagnosed de novo FL patients with available peripheral blood T lymphocyte subsets who were randomized to different sets (177 in the training set and 75 in the internal validation set). A nomogram and a novel immune-clinical prognostic index (ICPI) were established according to multivariate Cox regression analysis for progression-free survival (PFS). The concordance index (C-index), Akaike’s information criterion (AIC), and likelihood ratio chi-square were employed to compare the ICPI’s discriminatory capability and homogeneity to that of FLIPI, FLIPI2, and PRIMA-PI. Additional external validation was performed using a dataset (n = 157) from other four centers.ResultsIn the training set, multivariate analysis identified five independent prognostic factors (Stage III/IV disease, elevated lactate dehydrogenase (LDH), Hb <120g/L, CD4+ <30.7% and CD8+ >36.6%) for PFS. A novel ICPI was established according to the number of risk factors and stratify patients into 3 risk groups: high, intermediate, and low-risk with 4-5, 2-3, 0-1 risk factors respectively. The hazard ratios for patients in the high and intermediate-risk groups than those in the low-risk were 27.640 and 2.758. The ICPI could stratify patients into different risk groups both in the training set (P < 0.0001), internal validation set (P = 0.0039) and external validation set (P = 0.04). Moreover, in patients treated with RCHOP-like therapy, the ICPI was also predictive (P < 0.0001). In comparison to FLIPI, FLIPI2, and PRIMA-PI (C-index, 0.613-0.647), the ICPI offered adequate discrimination capability with C-index values of 0.679. Additionally, it exhibits good performance based on the lowest AIC and highest likelihood ratio chi-square score.ConclusionsThe ICPI is a novel predictive model with improved prognostic performance for patients with de novo FL treated with R-CHOP/CHOP chemotherapy. It is capable to be used in routine practice and guides individualized precision therapy.

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Chemometric-Assisted Spectrophotometric Method for the Simultaneous Determination of Ciprofloxacin and Doxycycline Hyclate in Pharmaceutical Formulations

Journal of Analytical Methods in Chemistry ◽

10.1155/2018/9538435 ◽

2018 ◽

Vol 2018 ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Tadele Eticha ◽

Getu Kahsay ◽

Fitsum Asefa ◽

Teklebrhan Hailu ◽

Hailekiros Gebretsadik ◽

...

Keyword(s):

External Validation ◽

Principal Component Regression ◽

Principal Component ◽

Pharmaceutical Formulations ◽

Uv Spectra ◽

Doxycycline Hyclate ◽

Pharmaceutical Dosage Forms ◽

Calibration Models ◽

Validation Set

Two chemometrics methods—principal component regression and partial least squares—were developed for simultaneous spectrophotometric estimation of ciprofloxacin and doxycycline hyclate in pharmaceutical dosage forms without any pretreatment. The UV spectra of both drugs were recorded at concentrations within their linear ranges between 200 and 400 nm with the intervalsλ= 2 nm at 100 wavelengths in distilled water. Beer’s law was obeyed for both drugs in the concentration ranges of 1–10 μg/mL for ciprofloxacin and 5–25 μg/mL for doxycycline hyclate. Two sets of standard mixtures, 25 as a calibration set and 9 as a validation set, were prepared. The calibration models were evaluated by cross-validation and external validation over synthetic mixtures. The optimized models were successfully applied for chemometric analysis of ciprofloxacin and doxycycline hyclate in synthetic and pharmaceutical mixtures with satisfactory accuracy (recovery values from 97.50% to 101.87%) and precision (RSD < 2%).

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