2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer

Background Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. Purpose To explore the use of dynamic contrast‐enhanced (DCE)‐magnetic resonance imaging (MRI)‐based radiomics for preoperative prediction of LVI in invasive breast cancer. Study Type Prospective. Population Ninety training cohort patients (22 LVI‐positive and 68 LVI‐negative) and 59 validation cohort patients (22 LVI‐positive and 37 LVI‐negative) were enrolled. Field Strength/Sequence 1.5 T and 3.0 T, T1‐weighted DCE‐MRI. Assessment Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE‐MRI. A radiomics signature was constructed in the training cohort with 10‐fold cross‐validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. Statistical Tests Mann–Whitney U‐test, chi‐square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). Results Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. Data Conclusion The DCE‐MRI‐based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847–857.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

Liu

Bao

et al. 2019

“…Some previous studies used automatic or semiautomatic segmentation methods because manual segmentation is time‐consuming and is susceptible to interobserver variability . Nonetheless, manual delineation of the tumors by experienced radiologist is standard clinical routine, which has been applied in many radiomics studies . Moreover, MRI has a better soft tissue resolution, allowing tumor borders to be delineated more accurately than CT or PET/CT .…”

Section: Discussionmentioning

confidence: 99%

“…11,12,35 Nonetheless, manual delineation of the tumors by experienced radiologist is standard clinical routine, which has been applied in many radiomics studies. 23,[36][37][38][39] Moreover, MRI has a better soft tissue resolution, allowing tumor borders to be delineated more accurately than CT or PET/CT. 25 Given that reason, we handcrafted all segmentations on tumors slice by slice and achieved satisfactory inter-and intra-observer reproducibility.…”

Section: Discussionmentioning

confidence: 99%

A Triple‐Classification Radiomics Model for the Differentiation of Primary Chordoma, Giant Cell Tumor, and Metastatic Tumor of Sacrum Based on T2‐Weighted and Contrast‐Enhanced T1‐Weighted MRI

Yin

Mao

Zhao

et al. 2018

Background Preoperative differentiation between primary sacral chordoma (SC), sacral giant cell tumor (SGCT), and sacral metastatic tumor (SMT) is important for treatment decisions. Purpose To develop and validate a triple‐classification radiomics model for the preoperative differentiation of SC, SGCT, and SMT based on T2‐weighted fat saturation (T2w FS) and contrast‐enhanced T1‐weighted (CE T1w) MRI. Study type Retrospective. Population A total of 120 pathologically confirmed sacral patients (54 SCs, 30 SGCTs, and 36 SMTs) were retrospectively analyzed and divided into a training set (n = 83) and a validation set (n = 37). Field strength/sequence The 3.0T axial T2w FS and CE T1w MRI. Assessment Morphology, intensity, and texture features were assessed based on Formfactor, Haralick, Gray‐level co‐occurrence matrix (GLCM), Gray‐level run‐length matrix (GLRLM), histogram. Statistical tests Analysis of variance, least absolute shrinkage and selection operator (LASSO), Pearson correlation, Random Forest (RF), area under the receiver operating characteristic curve (AUC) and accuracy analysis. Results The median age of SGCT (33.5, 25.3–45.5) was significantly lower than those of SC (58.0, 48.8–64.3) and SMT (59.0, 46.3–65.5) groups (χ2 = 37.6; P < 0.05). No significant difference was found when compared in terms of genders, tumor locations, and tumor sizes of SC, SGCT, and SMT (χgender2=3.75,χlocation2=2.51,χsize2=5.77; P1 = 0.15, P2 = 0.29, P3 = 0.06). For the differential value, features extracted from joint T2w FS and CE T1w images outperformed those from T2w FS or CE T1w images alone. Compared with CE T1w images, features derived from T2w FS images yielded higher AUC in both training and validating set. The best performance of radiomics model based on joint T2w FS and CE T1w images reached an AUC of 0.773, an accuracy of 0.711. Data conclusion Our 3.0T MRI‐based triple‐classification radiomics model is feasible to differentiate SC, SGCT, and SMT, which may be applied to improve the precision of preoperative diagnosis in clinical practice. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:752–759.

“…Whether these image signs could be used for TFTY BCa recurrence prediction remains inconclusive to date. In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, as well as predicting mental disorders schizophrenia …”

mentioning

confidence: 99%

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Background Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type Retrospective. Population Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W), multi‐b‐value diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics features were extracted from the T2W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine‐based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle‐invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics‐clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. Statistical Tests Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics‐clinical nomogram than using the radiomics or clinical model alone. Data Conclusion The proposed radiomics‐clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893–1904.