Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Preoperative discrimination between nonmuscle‐invasive bladder carcinomas (NMIBC) and the muscle‐invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). Purpose To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. Study Type Retrospective, radiomics. Population Fifty‐four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. Field Strength/Sequence 3.0T MRI/T 2 ‐weighted (T 2 W) and multi‐b‐value diffusion‐weighted (DW) sequences. Assessment A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T 2 W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM‐RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. Statistical Tests Chi‐square test and Student's t ‐test were applied on clinical characteristics to analyze the significant differences between patient groups. Results Of the 1104 features, an optimal subset involving 19 features was selected from T 2 W and DW sequences, which outperformed the other two subsets selected from T 2 W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM‐RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. Data Conclusion The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T 2 W sequence or DW sequence only. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489–1498.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it