scholarly journals Stereotactic Body Radiotherapy for Hepatocellular Carcinoma: Current Evidence and the Feasibility of Radiomics-based Predictive Models

2020 ◽  
Author(s):  
Yang-Hong Dai ◽  
Po-Chien Shen ◽  
Wei-Chou Chang ◽  
Chen-Hsiang Lo ◽  
Jen-Fu Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Local Control ◽  
Predictive Models ◽  
Stereotactic Body Radiotherapy ◽  
Current Knowledge ◽  
Area Under The Curve ◽  
Current Evidence ◽  
Support Vector ◽  
Svm Classifier

Abstract Background : Stereotactic body radiotherapy (SBRT) is an effective but less focused alternative for treatment of hepatocellular carcinoma (HCC). To date, a personalized model for predicting therapeutic response is lacking. This study aimed to review current knowledge and to propose a radiomics-based machine-learning (ML) strategy for local response (LR) prediction. Methods : We searched the literature for studies conducted between January 1993 and August 2019 that used > 100 patients. Additionally, 172 HCC patients in our hospital were retrospectively analyzed between January 2007 and December 2016. In the radiomic analysis, 41 treated tumors were contoured and 46 radiomic features were extracted. Results : The 1-year local control was 85.4% in our patient cohort, comparable with current results (87-99%). The Support Vector Machine (SVM) classifier, based on computed tomography (CT) scans in the A phase processed by equal probability (Ep) quantization with 8 gray levels, showed the highest mean F1 score (0.7995) for favorable LR within 1 year (W1R), at the end of follow-up (EndR), and condition of in-field failure-free (IFFF). The area under the curve (AUC) for this model was 92.1%, 96.3%, and 99.2% for W1R, EndR, and IFFF, respectively. Conclusions : SBRT has high 1-year local control and our study sets the basis for constructing predictive models for HCC patients receiving SBRT.

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma

2020 ◽  
Vol 9 (7) ◽  
pp. 2156
Author(s):  
Mi-ri Kwon ◽  
Jung Hee Shin ◽  
Hyunjin Park ◽  
Hwanho Cho ◽  
Eunjin Kim ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Distant Metastasis ◽  
Area Under The Curve ◽  
Distant Metastases ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Thyroid Ultrasound ◽  
Widely Invasive ◽  
Radiomics Signature

We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (p < 0.05). Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.

scholarly journals Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

2017 ◽  
Vol 131 (13) ◽  
pp. 1465-1481 ◽  
Author(s):  
Víctor González-Castro ◽  
María del C. Valdés Hernández ◽  
Francesca M. Chappell ◽  
Paul A. Armitage ◽  
Stephen Makin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Human Performance ◽  
Small Vessel Disease ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Svm Classifier ◽  
Perivascular Spaces ◽  
Scale Invariant

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

Prognosis of hepatic failure with the albumin-bilirubin model for hepatocellular carcinoma after stereotactic body radiotherapy with and without transplant.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 384-384
Author(s):  
Shaakir Hasan ◽  
Alexander V. Kirichenko ◽  
Paul Renz ◽  
Vijay Kudithipudi ◽  
Molly Vincent ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Hepatic Failure ◽  
Stereotactic Body Radiotherapy ◽  
Disease Free Survival ◽  
Median Dose ◽  
Free Survival ◽  
Prognostic Assessment ◽  
Disease Free

384 Background: The Albumin-Bilirubin (ALBI) model is a validated prognostic assessment of cirrhosis in hepatocellular carcinoma (HCC), stratifying patients to grades 1(ALBI-1), 2(ALBI-2), or 3(ALBI-3). We reported that ALBI distinguishes patients at higher risk for hepatic failure(HF) after stereotactic body radiotherapy (SBRT) within the Child Pugh(CP) A population. We now apply the ALBI model to both CP-A and CP-B patients after SBRT with or without orthotropic liver transplant (OLT), and assess its prognostic capability of overall survival (OS) and HF relative to the CP model. Methods: From 2009-2017, 68 patients with 81 HCC lesions and CP-A (45) or CP-B (23) cirrhosis completed SBRT in this IRB approved study. The median dose was 45 Gy (35 - 57 Gy) in 4-7 fractions. Initial ALBI and CP scores were measured against OS and progression of CP class, which was recorded every 3-4 months. Median follow-up = 18 months. Results: The median age = 62 and tumor size = 3.5 cm (1.1 Ð 11 cm). 26 patients were ALBI-1, 31 ALBI-2, and 11 ALBI-3 prior to SBRT. For all patients, 2-year local control was 96%. 1 and 2 year OS was 77% and 54%, disease free survival was 71% and 40%, and freedom from CP progression was 71% and 56%, respectively. OS was significantly different between ALBI-1, ALBI-2, and ALBI-3 patients (P = 0.01), as was progression of CP class (P<0.001). When stratified by initial CP class, there were no significant differences in survival or CP progression [Table 1]. In a subset of 37 CP-A and 15 CP-B without OLT, rates of progressive cirrhosis were better predicted by ALBI (P<0.001) than CP class (P=0.09). Conclusions: Compared to the CP model, the ALBI index more precisely predicted HF and OS in HCC patients for both early and intermediate cirrhosis. Its application may help better select candidates for OLT after SBRT, who may be at higher risk for HF than initially predicted. [Table: see text]

Safety and efficacy of accelerated hypofractionation and stereotactic body radiotherapy for hepatocellular carcinoma in the setting of advanced liver dysfunction.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 403-403
Author(s):  
Nima Nabavizadeh ◽  
Joseph Waller ◽  
Robert Fain ◽  
Yiyi Chen ◽  
Catherine Degnin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Local Control ◽  
Liver Dysfunction ◽  
Stereotactic Body Radiotherapy ◽  
Effective Treatment Option ◽  
Local Progression ◽  
Grade 5 ◽  
Secondary Endpoints ◽  
Critical Organ ◽  
Grade 3

403 Background: To report toxicities and outcomes for stereotactic body radiotherapy (SBRT) and accelerated hypofractionated radiotherapy (AHRT) in patients with Child-Pugh (CP) A/B/C and Albumin-Bilirubin (ALBI) score 1/2/3 hepatocellular carcinoma (HCC). Methods: We retrospectively reviewed 151 patients with HCC treated with SBRT (50 Gy in 5 fractions) or AHRT (45 Gy in 18 fractions) between 2007 and 2015. Primary endpoint was incidence of grade 3 or higher toxicities within 6 months of radiotherapy (RT). Patients were censored for toxicity upon local progression, further liver-directed therapy, or if they exhibited grade 3 or higher toxicities prior to RT, unless RT elevated the grading or a new toxicity class was observed. Secondary endpoints of overall survival and local control were calculated. Results: Median follow-up was 11 months (1 – 90 months). Most received SBRT (72%), while 28% received AHRT due to size criteria ( > 5 cm) or proximity to a critical organ-at-risk. Grade 3 or higher hyperbilirubinemia and hypoalbuminemia was greater in the CP-B8/B9/C patients (42% and 22%) or ALBI-3 patients (45% and 31%) compared to patients with CP-A/B7 (11% and 4%, p < 0.001) or ALBI-1/2 (14% and 4%, p < 0.001). For all other toxicity classes, no difference between liver functionality groups was seen. Eleven grade 4 and no grade 5 toxicities were observed. For all pts, 1- and 2-year treated-lesion local control (LC) rates were greater for SBRT as compared to AHRT (2-year LC 95% vs. 66%, p < 0.0001). When excluding patients with planning treatment volumes > 115 cc (equivalent to a 6 cm sphere), SBRT still yielded superior outcomes. Conclusions: Other than higher rates of grade 3+ hypoalbuminemia and hyperbilirubinemia, highly conformal RT appears to be a potentially safe and effective treatment option for HCC patients with advanced liver dysfunction. Compared to AHRT, SBRT is associated with superior local control.

scholarly journals Stereotactic body radiotherapy for patients with oligometastases from colorectal cancer: risk-adapted dose prescription with a maximum dose of 83–100 Gy in five fractions

2016 ◽  
Vol 57 (4) ◽  
pp. 400-405 ◽  
Author(s):  
Atsuya Takeda ◽  
Naoko Sanuki ◽  
Yuichiro Tsurugai ◽  
Yohei Oku ◽  
Yousuke Aoki
Keyword(s):  
Colorectal Cancer ◽  
Local Control ◽  
Stereotactic Body Radiotherapy ◽  
Pulmonary Metastases ◽  
Maximum Dose ◽  
Survival Rates ◽  
Target Volume ◽  
High Dose ◽  
Concurrent Administration

Abstract We previously reported that the local control of pulmonary metastases from colorectal cancer (CRC) following stereotactic body radiotherapy (SBRT) with moderate prescription dose was relatively worse. We investigated the treatment outcomes and toxicities of patients with oligometastases from CRC treated by SBRT using risk-adapted, very high- and convergent-dose regimens. Among patients referred for SBRT from August 2011 to January 2015, those patients were extracted who had liver or pulmonary metastases from CRC, and they were treated with a total dose of 50–60 Gy in five fractions prescribed to the 60% isodose line of the maximum dose covering the surface of the planning target volume. Concurrent administration of chemotherapy was not admitted during SBRT, while neoadjuvant or adjuvant chemotherapy was allowed. A total of 21 patients (12 liver, 9 lung) with 28 oligometastases were evaluated. The median follow-up duration was 27.5 months (range: 6.5–43.3 months). Four patients were treated with SBRT as a series of initial treatments, and 17 patients were treated after recurrent oligometastases. The local control rates at 1 and 2 years from the start of SBRT were 100%. The disease-free and actuarial overall survival rates were 62% and 55%, and 79% and 79%, respectively. No severe toxicities (≥grade 3) occurred during follow-up. The outcomes following high-dose SBRT were excellent. This treatment can provide an alternative to the surgical resection of oligometastases from CRC. Prospective studies are needed to validate the effectiveness of SBRT.

scholarly journals Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

2021 ◽  
Author(s):  
Le-le Song ◽  
Shun-jun Chen ◽  
Wang Chen ◽  
Zhan Shi ◽  
Xiao-dong Wang ◽  
...  
Keyword(s):  
Pleomorphic Adenoma ◽  
Predictive Value ◽  
Current Knowledge ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Model

Abstract Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p<0.001) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

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