Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Magnetic Resonance Imaging
2.2. Segmentation, Feature Extraction
- Shape features: These describe the geometric properties of the region of interest (ROI), such as the surface area, total volume, diameter, elongation, sphericity, and surface-to-volume ratio.
- First-order statistics (histogram-based features): These detail the distribution of voxel intensities within the image ROI, using conventional parameters like energy, entropy, mean, interquartile range, skewness, kurtosis, and uniformity.
- Second-order statistics (textural features): These capture the statistical interrelationships between neighboring voxels. Notable methods include:
- ○
- Gray-level Cooccurrence Matrix (GLCM): Analyzes the spatial distribution of gray-level intensities in a 3D image.
- ○
- Gray-Level Run-length Matrix (GLRLM): Measures contiguous voxels with the same gray-level value, characterizing the gray-level run lengths in various directions.
- ○
- Gray-Level Size-Zone Matrix (GLSZM): Quantifies the zones of connected voxels sharing the same gray-level intensity in a 3D image.
- ○
- Neighboring Gray-Tone Difference Matrix (NGTMD): Calculates the difference between a voxel’s gray value and the average gray value of its neighbors within a specified distance.
- ○
- Gray-level Dependence Matrix (GLDM): Assesses the number of connected voxels within a certain distance that depend on the center voxel.
2.3. Radiomics Analysis and Model Development
2.4. Clinical Nomograms
- Briganti: Preoperative PSA, clinical stage T, Gleason score of the biopsy, percentage of positive cores with a high level of prostate cancer, percentage of positive cores with a low level of prostate cancer.
- MSKCC: Age, thickness, Clark level, localization, ulceration.
- Yale: PSA, Gleason score, clinical T stage.
- Roach: PSA, Gleason score.
3. Results
4. Discussion
- The Briganti model [23] is used for cancer involvement and grading heterogeneity in biopsy samples and improves the accuracy in estimating the risk of lymph node invasion (LNI), suggesting changes in staging approaches.
- Due to the risk of overtreatment with the only Briganti model, the MSKCC (Memorial Sloan Kattering Cancer Center) calculate another nomogram with a minimalist approach. In this case, the evaluation includes only the PSA, age, and biopsy Gleason score.
- The Yale is a linear model based on PSA levels, the T stage, and the Gleason score. This model more successfully classifies high-risk categories patients (>15%). Unlike previous models, Yale does not underestimate the risks associated with lymph node involvement.
- The Roach formula [24] that includes only the PSA and the Gleason score can be used for the evaluation of lymph node involvement and the seminal vesicle and capsular involvement.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Number |
---|---|
Number of patients | 95 |
Patients with metastatic nodule at lymphadenectomy | 35 |
Patients without metastatic nodule at lymphadenectomy | 60 |
Race | Caucasian |
Age | 40–80 |
PSA [ng/mL] (Median, range) | 4.5 (7.1) |
Period of mp-MRI | 2016–2023 |
Gleason Grade (Mediana) | 7 |
Tumor target Zone Peripheral | 33 |
Tumor target Zone Transition | 12 |
Disease Grade | T3 (16) T2 (10) T3,5 (19) |
(a) | ||||
# | Features | LogRegCoef | SVMCoef | RFImportance |
1 | T2_noduloglcmContrast | 0.0084 | 0.0427 | 0.0457 |
2 | T2_nodulofirstorderKurtosis | −0.5975 | −0.2378 | 0.0395 |
3 | T2_nodulofirstorderMeanAbsoluteDeviation | −0.0009 | 0.0231 | 0.0364 |
4 | T2_nodulofirstorderVariance | 0.4459 | 0.1540 | 0.0332 |
5 | T2_noduloglcmIdm | 0.1337 | 0.0445 | 0.0320 |
6 | T2_noduloshapeSphericity | 0.0420 | 0.0148 | 0.0000 |
7 | T2_nodulofirstorder10Percentile | −0.1215 | −0.0385 | 0.0000 |
8 | T2_noduloglcmDifferenceVariance | −0.3277 | −0.1183 | 0.0000 |
9 | T2_noduloglrlmGrayLevelNonUniformityNormalized | 0.0420 | 0.0148 | 0.0000 |
10 | T2_noduloglrlmLowGrayLevelRunEmphasis | 0.0420 | 0.0148 | 0.0000 |
(b) | ||||
# | Feature | RFImportance | LogRegCoef | |
1 | DWIfirstorderEnergy | 0.0770 | 3.50 × 10−8 | |
2 | DWIglcmIdn | 0.0693 | −4.46 × 10−13 | |
3 | DWIglrlmRunLengthNonUniformityNormalized | 0.0663 | −1.39 × 10−13 | |
4 | DWIglszmGrayLevelNonUniformityNormalized | 0.0592 | −2.85 × 10−13 | |
5 | DWIgldmSmallDependenceEmphasis | 0.0507 | 9.34 × 10−15 | |
6 | DWIfirstorderMedian | 0.0481 | −2.17 × 10−11 | |
7 | DWIglrlmGrayLevelNonUniformityNormalized | 0.0445 | −2.95 × 10−13 | |
8 | DWIglcmImc1 | 0.0429 | 5.94 × 10−14 | |
9 | DWIglcmIdmn | 0.0423 | −3.91 × 10−13 | |
10 | DWIglcmCorrelation | 0.0410 | −1.85 × 10−13 | |
(c) | ||||
# | Feature | RF Importance | LogRegCoef | |
1 | ADCglszmGrayLevelNonUniformityNormalized | 0.0339 | 1.25 × 10−33 | |
2 | ADCglcmSumAverage | 0.0320 | −3.62 × 10−15 | |
3 | ADCshapeMeshVolume | 0.0297 | −1.07 × 10−15 | |
4 | ADCfirstorderUniformity | 0.0274 | −7.88 × 10−33 | |
5 | ADCshapeMajorAisLength | 0.0251 | 3.17 × 10−15 | |
6 | ADCfirstorderMeanAbsoluteDeviation | 0.0249 | 1.27 × 10−15 | |
7 | ADCshapeMaimum2DDiameterSlice | 0.0231 | 2.15 × 10−15 | |
8 | ADCglszmSmallAreaLowGrayLevelEmphasis | 0.0228 | 1.56 × 10−32 | |
9 | ADCglrlmRunVariance | 0.0222 | 9.95 × 10−32 | |
10 | ADCglcmImc1 | 0.0222 | 7.52 × 10−32 |
SEQUENCES | MODEL | Accuracy | AUC |
---|---|---|---|
T2 nod | Random Forest | 0.78 | 0.78 |
Logistic Regression | 0.78 | 0.78 | |
Support Vector Machine | 0.78 | 0.17 | |
DWI | Random Forest | 0.86 | 0.89 |
Logistic Regression | 0.78 | 0.67 | |
Support Vector Machine | 0.89 | 0.28 | |
ADC | Random Forest | 0.89 | 0.67 |
Logistic Regression | 0.67 | 0.67 | |
Support Vector Machine | 0.78 | 0.67 |
(a) | ||||
Model Comparison | AUC Radiomics Model | AUC Nomogram | Z-Score | p-Value |
LR vs. Briganti | 0.89 | 0.79 | 0.833 | 0.405 |
LR vs. Partin | 0.89 | 0.78 | 1.117 | 0.264 |
LR vs. MSKCC | 0.89 | 0.78 | 1.132 | 0.258 |
LR vs. YALE | 0.89 | 0.78 | 1.123 | 0.262 |
RF vs. Briganti | 0.78 | 0.79 | −0.345 | 0.730 |
RF vs. Partin | 0.78 | 0.78 | 0 | 1 |
RF vs. MSKCC | 0.78 | 0.78 | 0 | 1 |
RF vs. YALE | 0.78 | 0.78 | 0 | 1 |
SVM vs. Briganti | 0.17 | 0.79 | 1248.94 | <0.05 |
SVM vs. Partin | 0.17 | 0.78 | 1221.28 | <0.05 |
SVM vs. MSKCC | 0.17 | 0.78 | 1221.64 | <0.05 |
SVM vs. YALE | 0.17 | 0.78 | 1221.46 | <0.05 |
(b) | ||||
Model Comparison | AUC radiomics model | AUC nomogram | Z-Score | p-Value |
RF vs. Briganti | 0.89 | 0.79 | 2.00 | 0.0455 |
RF vs. Partin | 0.89 | 0.78 | 2.20 | 0.0278 |
RF vs. MSKCC | 0.89 | 0.78 | 2.20 | 0.0278 |
RF vs. YALE | 0.89 | 0.78 | 2.20 | 0.0278 |
LR vs. Briganti | 0.671 | 0.79 | 1.733 | 0.083 |
LR vs. Partin | 0.671 | 0.78 | 1.546 | 0.122 |
LR vs. MSKCC | 0.671 | 0.78 | 1.529 | 0.126 |
LR vs. YALE | 0.671 | 0.78 | 1.507 | 0.132 |
(c) | ||||
Model Comparison | AUC radiomics model | AUC nomogram | Z-score | p-value |
LR vs. Briganti | 0.67 | 0.79 | −0.439 | 0.661 |
LR vs. Partin | 0.67 | 0.78 | 0.039 | 0.969 |
LR vs. MSKCC | 0.67 | 0.78 | −0.028 | 0.978 |
LR vs. YALE | 0.67 | 0.78 | 0.065 | 0.948 |
RF vs. Briganti | 0.67 | 0.80 | −1.27 | 0.205 |
RF vs. Partin | 0.67 | 0.78 | −1.11 | 0.268 |
RF vs. MSKCC | 0.67 | 0.78 | −1.06 | 0.290 |
RF vs. YALE | 0.67 | 0.78 | −1.05 | 0.295 |
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Share and Cite
Santucci, D.; Ragone, R.; Vergantino, E.; Vaccarino, F.; Esperto, F.; Prata, F.; Scarpa, R.M.; Papalia, R.; Beomonte Zobel, B.; Grasso, F.R.; et al. Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features. Cancers 2024, 16, 2731. https://doi.org/10.3390/cancers16152731
Santucci D, Ragone R, Vergantino E, Vaccarino F, Esperto F, Prata F, Scarpa RM, Papalia R, Beomonte Zobel B, Grasso FR, et al. Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features. Cancers. 2024; 16(15):2731. https://doi.org/10.3390/cancers16152731
Chicago/Turabian StyleSantucci, Domiziana, Raffaele Ragone, Elva Vergantino, Federica Vaccarino, Francesco Esperto, Francesco Prata, Roberto Mario Scarpa, Rocco Papalia, Bruno Beomonte Zobel, Francesco Rosario Grasso, and et al. 2024. "Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features" Cancers 16, no. 15: 2731. https://doi.org/10.3390/cancers16152731