Conditional survival of stage III non-seminoma testis cancer patients

Incesu, Reha‐Baris; Barletta, Francesco; Tappero, Stefano; Morra, Simone; Garcia, Cristina Cano; Scheipner, Lukas; Piccinelli, Mattia Luca; Tian, Zhe; Saad, Fred; Shariat, Shahrokh F.; Cobelli, O. De; Ahyai, Sascha; Chun, Felix K.‐H.; Longo, Nicola; Terrone, Carlo; Briganti, Alberto; Tilki, Derya; Graefen, Markus; Karakiewicz, Pierre I.

doi:10.1016/j.urolonc.2023.06.005

Cited by 4 publications

(10 citation statements)

References 13 publications

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“…Our model enables us to identify high-risk patients and enhance the treatment process by providing real-time prognosis evaluation. Furthermore, through the use of techniques like the C-index, calibration plot, ROC analysis, and DCA analysis [9,28,29], we have veri ed the outstanding predictive capabilities and practical value of our newly developed CS-nomogram model. As a result, the CS-based nomogram offers numerous advantages over conventional methods, particularly in terms of its ability to provide real-time responses in survival analysis.…”

Section: Discussionmentioning

confidence: 89%

“…Traditional methods for survival analysis have limitations when applied to real-time data [9,19,27]. To address this issue, this study utilized CS analysis to improve the real-time survival estimation capability of the nomogram model.…”

Section: Discussionmentioning

confidence: 99%

“…To address this issue, this study utilized CS analysis to improve the real-time survival estimation capability of the nomogram model. As a result, a new and updated CS-nomogram was developed [9], which provides real-time survival information for MCC survivors. Our model enables us to identify high-risk patients and enhance the treatment process by providing real-time prognosis evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, conditional survival (CS) assessment has become an additional means of dynamically determining the prognosis of cancer survivors [9,10]. CS estimation can offer more precise prognostic data to clinicians and patients, but it fails to take into account the clinicopathological characteristics of patients [11].…”

Section: Introductionmentioning

confidence: 99%

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Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

Zhang,

Xiang,

Chen

et al. 2023

Preprint

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Background: Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. Methods: This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram. Results: A total of 3843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival.The rates progressed from an initial 41% to 50%, 61%, 70%, 78%, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. Conclusion: CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a real-time estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

Zhang,

Xiang,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Recently, conditional survival (CS) assessment has become an additional means of dynamically determining the prognosis of cancer survivors ( 10 , 11 ). CS estimation can offer more precise prognostic data to clinicians and patients, but it fails to take into account the clinicopathological characteristics of patients ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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BackgroundMerkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual’s prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.MethodsThis study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient’s survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram.ResultsA total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41–50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.ConclusionCS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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Contemporary conditional cancer‐specific survival rates in surgically treated nonmetastatic primary urethral carcinoma

Morra,

Scheipner,

Baudo

et al. 2024

Journal of Surgical Oncology

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BackgroundWe examined the effect of disease‐free interval (DFI) duration on cancer‐specific mortality (CSM)‐free survival, otherwise known as the effect of conditional survival, in radical urethrectomy nonmetastatic primary urethral carcinoma (PUC) patients.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database 2000–2020, patient (age, sex, race/ethnicity, and marital status) and tumor (stage and histology) characteristics, as well as systemic therapy exposure status of nonmetastatic PUC patients were tabulated. Conditional survival estimates at 5‐year were assessed based on DFI duration and according to stage at presentation (T1 –2N0 vs. T3–4N0–2).ResultsOf all 512 radical urethrectomy PUC patients, 278 (54%) harbored T1–2N0 stage versus 234 (46%) harbored T3–4N0–2 stage. In 512 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 61.8%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 85.6%. In 278 T1–2N0 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 68.4%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 86.9%. In 234 T3–4N0–2 PUC patients, 5‐year CSM‐free survival at initial diagnosis was 53.8%. Provided a DFI duration of 36 months, 5‐year CSM‐free survival was 83.6%.ConclusionsAlthough intuitively, clinicians and patients are well aware of the concept that increasing DFI duration improves survival probability, only a few clinicians can accurately estimate the magnitude of survival improvement, as was done within the current study. Such information is crucial to survivors, especially in those diagnosed with rare malignancies, where the survival estimation according to DFI duration is even more challenging.

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Conditional survival of stage III non-seminoma testis cancer patients

Cited by 4 publications

References 13 publications

Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

Conditional survival analysis and dynamic prediction of long-term survival in Merkel Cell Carcinoma patients

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Contemporary conditional cancer‐specific survival rates in surgically treated nonmetastatic primary urethral carcinoma

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