PD33-05 PROPOSAL AND INTERNAL VALIDATION OF A NOMOGRAM FOR THE PREDICTION OF LOCAL RECURRENCE-FREE SURVIVAL AFTER PERCUTANEOUS ABLATION FOR cT1 RENAL MASSES
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy II (PD33)1 May 2024PD33-05 PROPOSAL AND INTERNAL VALIDATION OF A NOMOGRAM FOR THE PREDICTION OF LOCAL RECURRENCE-FREE SURVIVAL AFTER PERCUTANEOUS ABLATION FOR cT1 RENAL MASSES Angelo Mottaran, Luigi Nocera, Lorenzo Bianchi, Stefano Luzzago, Marco Bandini, Pietro Piazza, Antonio Celia, Carla Serra, Alberta Cappelli, Antonio De Cinque, Francesco Modestino, Rita Golfieri, Gennaro Musi, Andrea Gallina, Francesco De Cobelli, Giovanni Mauri, Franco Orsi, Umberto Capitanio, Riccardo Schiavina, Ottavio De Cobelli, Francesco Montorsi, and Eugenio Brunocilla Angelo MottaranAngelo Mottaran , Luigi NoceraLuigi Nocera , Lorenzo BianchiLorenzo Bianchi , Stefano LuzzagoStefano Luzzago , Marco BandiniMarco Bandini , Pietro PiazzaPietro Piazza , Antonio CeliaAntonio Celia , Carla SerraCarla Serra , Alberta CappelliAlberta Cappelli , Antonio De CinqueAntonio De Cinque , Francesco ModestinoFrancesco Modestino , Rita GolfieriRita Golfieri , Gennaro MusiGennaro Musi , Andrea GallinaAndrea Gallina , Francesco De CobelliFrancesco De Cobelli , Giovanni MauriGiovanni Mauri , Franco OrsiFranco Orsi , Umberto CapitanioUmberto Capitanio , Riccardo SchiavinaRiccardo Schiavina , Ottavio De CobelliOttavio De Cobelli , Francesco MontorsiFrancesco Montorsi , and Eugenio BrunocillaEugenio Brunocilla View All Author Informationhttps://doi.org/10.1097/01.JU.0001008912.25331.d7.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Despite local tumor ablation (LTA) for the treatment of cT1 renal masses is increasing, the role of the different techniques used in influencing local recurrence is still unclear. We aim to develop a nomogram predicting local recurrence-free survival after different type of percutaneous LTA. METHODS: We identified 433 patients who underwent percutaneous LTA with radiofrequency (RFA), cryoblation (Cryo) or microwave (MW) ablation at four tertiary referral centers for cT1 renal masses. All masses received a biopsy before treatment. Patients', as well as renal masses' characteristics were collected. The primary end point was local recurrence on follow-up imaging. Statistical analyses consisted of two steps. First, a nomogram predicting local recurrence was built relying on multivariate Cox regression analysis. Second, performance characteristics of the nomogram were assessed at a threshold of 60 months, and compared to model exclusively based on clinical size and tumor's histology, using Heagerty's C and decision curve analysis (DCA). RESULTS: Overall, 393 (90.8%) patients had a positive biopsy for renal cancer while 40 (9.2%) patients had an undetermined biopsy. Overall, 172 (39.7%), 123 (28.4%), 138 (31.9%) patients underwent Cryo, MW and RFA, respectively. No differences in terms of age, CCI, BMI, clinical size (cm) and histology at pre-ablative biopsy were recorded among the three groups (all p>0.05). Overall, 53 patients (12.2%) recurred after a median time of 34 (IQR 18-60) months. The nomogram relied on the following variables: tumor size (cm), histology (malignant or undetermined), type of ablative procedure (MW, Cryo or RFA), polar involvement, >50% vs ≤50% exophytic rate, rim location (lateral or medial), sinus involvement and BMI (Figure 1). The newly developed nomogram yielded an area under the curve of 0.82 vs 0.73 for the model based on clinical size and histology. Moreover, the new nomogram also exhibited greater net-benefit across all threshold probabilities at DCA. CONCLUSIONS: The proposed nomogram to predict local recurrence after different techniques of LTA for renal cancer provides valuable information for both patient's and technique's selection for focal ablation of renal masses. Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e711 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Angelo Mottaran More articles by this author Luigi Nocera More articles by this author Lorenzo Bianchi More articles by this author Stefano Luzzago More articles by this author Marco Bandini More articles by this author Pietro Piazza More articles by this author Antonio Celia More articles by this author Carla Serra More articles by this author Alberta Cappelli More articles by this author Antonio De Cinque More articles by this author Francesco Modestino More articles by this author Rita Golfieri More articles by this author Gennaro Musi More articles by this author Andrea Gallina More articles by this author Francesco De Cobelli More articles by this author Giovanni Mauri More articles by this author Franco Orsi More articles by this author Umberto Capitanio More articles by this author Riccardo Schiavina More articles by this author Ottavio De Cobelli More articles by this author Francesco Montorsi More articles by this author Eugenio Brunocilla More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".