Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Image Acquisition and Analysis
2.3. Sarcopenia and Response to Therapy Definition
2.4. Statistical Analysis
3. Results
3.1. Demographic, Tumor- and Sarcopenia-Related Characteristics of the Study Population
3.2. Correlation between AI Skeletal Muscle Index (SMI-L3) and Anthropomorphic Sarcopenia-Related Variables Pre-/Post-Systemic Treatment
3.3. Baseline and Early Predictors of Clinical Benefit Measured at the Completion of Systemic Therapy
3.4. Baseline and Early Determinates for Overall Survival
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | No Sarcopenic Status, Baseline (By SMI-L3 Cut-Off) | Sarcopenic Status, Baseline (By SMI-L3 Cut-Off) | p-Value * | No SARCOPENIC Status, after CHT (By SMI-L3 Cut-Off) | Sarcopenic Status, after CHT (By SMI-L3 Cut-Off) | p-Value * |
---|---|---|---|---|---|---|
Sample size, n (%) | 46 (47.4) | 51 (52.6) | 45 (46.4) | 52 (53.6) | ||
Demographics and tumor-related features | ||||||
Age y, median (IQR) | 73 (64–76) | 69 (64–74) | 0.414 | 73 (66–76) | 68 (64–74) | 0.126 |
Age y, n (%) | ||||||
<70 y | 19 (41.3) | 30 (58.8) | 0.105 | 17 (37.8) | 32 (61.5) | 0.025 |
≥70 y | 27 (58.7) | 21 (42.2) | 28 (62.2) | 20 (38.5) | ||
Gender, n (%) | ||||||
Male | 36 (78.3) | 34 (66.7) | 0.259 | 11 (24.4) | 16 (30.8) | 0.506 |
Female | 10 (27.7) | 17 (33.3) | 34 (75.6) | 36 (69.2) | ||
ECOG PS, n (%) | ||||||
<2 | 39 (84.8) | 40 (78.4) | 0.447 | 38 (84.4) | 41 (78.8) | 0.603 |
≥2 | 7 (15.2) | 11 (21.6) | 7 (15.6) | 11 (21.2) | ||
n. of Medications, n (%) | ||||||
<6 | 36 (78.3) | 42 (82.4) | 0.620 | 34 (75.6) | 44 (84.6) | 0.311 |
≥6 | 10 (21.7) | 9 (17.6) | 11 (24.4) | 8 (15.4) | ||
Primary location n (%) | ||||||
BCa | 24 (52.2) | 37 (72.5) | 0.049 | 24 (53.3) | 37 (71.2) | 0.096 |
UTUC | 21 (45.7) | 12 (23.5) | 20 (44.4) | 13 (25.0) | ||
Concomitant | 1 (2.2) | 2 (3.9) | 1 (2.2) | 2 (3.8) | ||
Oncologic stage, n (%) | ||||||
III | 22 (47.8) | 17 (33.3) | 0.155 | 22 (48.9) | 17 (32.7) | 0.146 |
IV | 24 (52.2) | 34 (66.7) | 23 (51.1) | 35 (67.3) | ||
Anthropometric measures | ||||||
Height, m | 1.70 (1.66–1.75) | 1.70 (1.62–1.75) | 0.753 | 1.70 (1.64–1.73) | 1.70 (1.64–1.75) | 0.677 |
Weight, kg | 77 (70–85.25) | 70 (60–75) | 0.001 | 77 (70–80.25) | 70 (60–80) | 0.005 |
BMI, kg/m2 | 26.3 (24.85–27.75) | 24.2 (21.9–26.5) | 0.001 | 26.3 (25.3–29.3) | 23.5 (21.7–26.4) | 0.007 |
SMA, cm2 | 179.7 (167.8–194) | 135.8 (114.8–155.2) | <0.0001 | 173.1 (157.2–193) | 133.7 (116–154.4) | <0.0001 |
SMI-L3, (cm2/m2) | 62 (57.8–67.1) | 48.6 (43.1–53) | <0.0001 | 59.9 (57.3–63.8) | 47.9 (43.4–50.9) | <0.0001 |
Subcutaneous fat, (cm2/m2) | 184.2 (133.6–221.8) | 135.2 (108.4–178.6) | 0.003 | 178.7 (130.7–215.1) | 143.1 (108.4–183.2) | 0.002 |
Visceral fat, (cm2/m2) | 207 (153.7–255.7) | 103 (67.9–175.7) | <0.0001 | 182.6 (140–218.4) | 137 (64.5–181.6) | <0.0001 |
Psoas muscle, (cm2/m2) | 21.7 (19.4–24.6) | 16.5 (13.3–20) | <0.0001 | 20.8 (18–22.8) | 16.1 (13.5–19) | <0.0001 |
Abdominal muscle, (cm2/m2) | 100.6 (89.6–109.2) | 71.5 (59.1–82.2) | <0.0001 | 93.4 (86.6–106.9) | 71.6 (59.7–80.9) | <0.0001 |
Long spine muscle, (cm2/m2) | 60.3 (54.8–64.1) | 45.5 (40.8–55) | <0.0001 | 57.9 (51.4–62.8) | 46.4 (40.7–55.2) | <0.0001 |
∆_SMI-L3, mean (SD) | −1.86 (5.78) | |||||
∆_Subcutaneous fat mean (SD) | 0.25 (24.93) | |||||
∆_Visceral fat, mean (SD) | −4.93 (42.17) | |||||
∆_ Psoas muscle, mean (SD) | −0.85 (2.56) | |||||
∆_ Abdominal muscle, mean (SD) | −3.02 (10.74) | |||||
∆_ Long spine muscle, mean (SD) | −1.06 (3.39) | |||||
Clinical outcomes | ||||||
Clinical Benefit, n (%) | ||||||
SD/PR/CR | 30 (65.2) | 25 (49) | 0.151 | 29 (64.4) | 26 (50.0) | 0.217 |
PD | 16 (34.8) | 26 (51) | 16 (35.6) | 26 (50.0) | ||
Survival n (%) | ||||||
Deceased | 37 (80.4) | 23 (45.1) | 0.001 | 35 (77.8) | 25 (48.1) | 0.003 |
Survivors | 9 (19.6) | 28 (54.9) | 10 (22.2) | 27 (51.9) |
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Borrelli, A.; Pecoraro, M.; Del Giudice, F.; Cristofani, L.; Messina, E.; Dehghanpour, A.; Landini, N.; Roberto, M.; Perotti, S.; Muscaritoli, M.; et al. Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation. Cancers 2023, 15, 2968. https://doi.org/10.3390/cancers15112968
Borrelli A, Pecoraro M, Del Giudice F, Cristofani L, Messina E, Dehghanpour A, Landini N, Roberto M, Perotti S, Muscaritoli M, et al. Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation. Cancers. 2023; 15(11):2968. https://doi.org/10.3390/cancers15112968
Chicago/Turabian StyleBorrelli, Antonella, Martina Pecoraro, Francesco Del Giudice, Leonardo Cristofani, Emanuele Messina, Ailin Dehghanpour, Nicholas Landini, Michela Roberto, Stefano Perotti, Maurizio Muscaritoli, and et al. 2023. "Standardization of Body Composition Status in Patients with Advanced Urothelial Tumors: The Role of a CT-Based AI-Powered Software for the Assessment of Sarcopenia and Patient Outcome Correlation" Cancers 15, no. 11: 2968. https://doi.org/10.3390/cancers15112968