Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. Patient Demographics
2.2. Association between Qualitative Delta and q-delta
2.3. Association between Quantitative Delta and nAUC
2.4. Inter- and Intrarater Variability Assessment
2.5. Association between the q-delta and Tumor Stromal Content
2.6. Association between q-delta and Clinical Outcome
3. Discussion
4. Materials and Methods
4.1. Patients
4.2. CT Acquisition
4.3. CT Analysis: Normalized Area under the Curve (nAUC)
4.4. CT Analysis: Qualitative Delta Scoring
4.5. CT Analysis: Quantitative Normalized Enhancement (q-delta)
4.6. Inter- and IntraRater Agreement
4.7. Histopathological Analysis
4.8. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Characteristic | MDACC Data | External Validation Data | ||
---|---|---|---|---|
D1 | D2 | D3 | ||
Training Set (n = 101) | Validation Set (n = 90) | OSU (n = 8) | TCIA (n = 8) | |
No. (%) | No. (%) | No. (%) | No. (%) | |
Age (median, range) | 64 (25–85) | 63 (42–82) | 67.5 (50–88) | 65 (34–80) |
Sex | ||||
Women | 46 (47) | 38 (42) | 3 (27) | 3 (37) |
Men | 55 (53) | 52 (58) | 5 (63) | 5 (63) |
Pathological T stage | ||||
T1 | 21 (21) | 24 (37) | 0 (0) | 0 (0) |
T2 | 67 (66) | 35 (54) | 0 (0) | 0 (0) |
T3 | 12 (12) | 6 (9) | 8 (100) | 8 (100) |
T4 | 1 (1) | - | 0 (0) | 0 (0) |
Pathological N stage | ||||
Negative (N0) | 19 (19) | 34 (52) | - | 1 (12) |
Positive (N1) | 30 (30) | 22 (34) | 3 (38) | 7 (88) |
Positive (N2) | 52 (51) | 9 (14) | 5 (62) | - |
Overall Stage | ||||
IA | 8 (8) | 15 (23) | - | - |
IB | 9 (9) | 16 (25) | - | - |
IIA | 2 (2) | 3 (5) | - | 1 (12) |
IIB | 30 (30) | 22 (34) | 3 (38) | 7 (88) |
III | 52 (51) | 9 (14) | 5 (62) | - |
IV | - | - | - | - |
Surgery | ||||
Yes | 101 (100) | 65 (72) | 8 (100) | 8 (100) |
No | - | 25 (28) | - | - |
Surgical margin | ||||
Negative (R0) | 87 (86) | 62 (95) | 2 (25) | 5 (62) |
Positive (R1) | 14 (14) | 3 (5) | 6 (75) | 3 (38) |
Adjuvant chemotherapy | ||||
Yes | 76 (75) | - | 6 (75) | NA* |
No | 25 (25) | - | 2 (25) | NA |
Adjuvant chemoradiation | ||||
Yes | 33 (33) | - | - | NA |
No | 68 (67) | - | 8 (100) | NA |
Variable | p-Value | AUC (95% CI) | Sensitivity (%) | Specificity (%) | Accuracy (%) | Misclassification (%) |
---|---|---|---|---|---|---|
Univariate logistic regression | ||||||
Enhancement AR-(NPP) | <0.0001 | 0.94 (0.90–0.99) | 89 | 85 | 87 | 13 |
Enhancement PV-(NPP) | <0.0001 | 0.91 (0.84–0.97) | 89 | 81 | 85 | 15 |
Enhancement AR-(AFF) | <0.0001 | 0.73 (0.63–0.84) | 65 | 74 | 70 | 30 |
Enhancement PV-(AFF) | 0.0003 | 0.70 (0.60–0.81) | 54 | 78 | 68 | 32 |
Multivariate logistic regression | ||||||
Training set (q-delta) | <0.0001 | 0.95 (0.91–0.99) | 91 | 89 | 91 | 9 |
Internal validation set (q-delta) | <0.0001 | 0.96 (0.92–1) | 100 | 83 | 93 | 7 |
External validation set (q-delta) | 0.03 | 0.90 (0.69–1) | 85 | 1 | 93 | 7 |
Characteristic | No. of Patients | Univariate Analysis | Multivariate Analysis | ||
---|---|---|---|---|---|
HR (95% CI) | p Value | HR (95% CI) | p Value | ||
q-delta | |||||
High | 43 | 2.6 (1.6–4.1) | <0.0001 | 2 (1.2–3.4) | 0.004 |
Low | 58 | - | - | - | - |
Age | 101 | 1 (0.9–1.03) | 0.3 | - | - |
Sex | |||||
Male | 55 | 0.69 (0.4–1.07) | 0.1 | 0.8 (0.5–1.3) | 0.5 |
Female | 46 | ||||
Surgical margin | |||||
Positive (R1) | 14 | 1.6 (0.7–2.8) | 0.18 | 1.3 (0.6–2.5) | 0.3 |
Negative (R0) | 87 | - | - | - | - |
Pathologic N Stage | |||||
N0 vs. N1 | 49 | 0.47 (0.21–0.97) | 0.04 | 0.4 (0.2–1.03) | 0.06 |
N1 vs. N2 | 82 | 0.6 (0.41–1.1) | 0.1 | 0.7 (0.4–1.1) | 0.2 |
N0 vs. N2 | 71 | 0.3 (0.1–0.6) | 0.0005 | 0.3 (0.1–0.6) | 0.002 |
Adjuvant chemotherapy | |||||
Yes | 76 | 0.5 (0.3–0.9) | 0.02 | 0.5 (0.3–0.9) | 0.02 |
No | 25 | - | - | - | - |
Univariate Analysis | Multivariate Analysis | ||||
---|---|---|---|---|---|
Characteristic | No. of Patients | HR (95% CI) | p Value | HR (95% CI) | p Value |
q-delta (†) | |||||
High | 52 | 2.4 (1.4–3.9) | 0.0003 | 1.9 (1.1–3.2) | 0.01 |
Low | 37 | - | - | - | - |
Surgery | |||||
Yes | 65 | 0.13 (0.07–0.2) | <0.0001 | 0.1 (0.06–0.2) | <0.0001 |
No | 25 | - | - | - | - |
q-delta (††) | |||||
High | 35 | 2.5 (1.4–4.6) | 0.0003 | 2.9 (1.6–5.5) | 0.001 |
Low | 30 | - | - | - | - |
Age | 65 | 1.02 (0.9–1.06) | 0.16 | 1 (0.4–7) | 0.3 |
Sex | |||||
Male | 36 | 1 (0.62–1.8) | 0.7 | - | - |
Female | 29 | ||||
Surgical margin | |||||
Positive (R1) | 62 | 1.3 (0.4–4.2) | 0.6 | - | - |
Negative (R0) | 3 | - | - | - | - |
N Stage | |||||
N0 vs. N1 | 56 | 1 (0.5–1.9) | 0.8 | - | - |
N1 vs. N2 | 31 | 0.6 (0.3–1.5) | 0.3 | - | - |
N0 vs. N2 | 41 | 0.7 (0.3–1.5) | 0.3 | - | - |
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Zaid, M.; Widmann, L.; Dai, A.; Sun, K.; Zhang, J.; Zhao, J.; Hurd, M.W.; Varadhachary, G.R.; Wolff, R.A.; Maitra, A.; et al. Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study. Cancers 2020, 12, 3656. https://doi.org/10.3390/cancers12123656
Zaid M, Widmann L, Dai A, Sun K, Zhang J, Zhao J, Hurd MW, Varadhachary GR, Wolff RA, Maitra A, et al. Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study. Cancers. 2020; 12(12):3656. https://doi.org/10.3390/cancers12123656
Chicago/Turabian StyleZaid, Mohamed, Lauren Widmann, Annie Dai, Kevin Sun, Jie Zhang, Jun Zhao, Mark W. Hurd, Gauri R. Varadhachary, Robert A. Wolff, Anirban Maitra, and et al. 2020. "Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study" Cancers 12, no. 12: 3656. https://doi.org/10.3390/cancers12123656