An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer

Domain: biology
Published: 2026-03-14
Editorial outcome: minor_revision

Abstract
Precision oncology relies on access to high-quality data for increasingly smaller patient subgroups. The international
atomCAT consortium investigates the potential of federated learning to support this, using anal cancer as a rare
cancer exemplar. Here, we show that federated multivariable Cox models trained across 14 centres (1,428 patients)
and externally validated in two additional centres (277 patients) achieve consistent calibration and discrimination
during leave-one-centre-out and external validation (c-indices 0.68-0.79). Lower T stage, absence of nodal
involvement, smaller tumour volume, female sex, younger age, and mitomycin- or cisplatin-based chemotherapy
are associated with improved overall survival. Lower T stage, smaller tumour volume, and female sex are associated
with improved locoregional control, while absence of nodal involvement and smaller tumour volume are associated
with better freedom from distant metastases. These findings demonstrate that federated learning enables robust,
privacy-preserving prognostic modelling for rare cancers using real-world data, supporting international
collaboration without data sharing.

Introduction
Precision oncology has brought increasing use of molecular profiling and sub-stratification, with more patients being
treated with targeted agents guided by tumour genetic profiles. Consequently, many previously deemed ‘common
cancers’ (e.g. lung cancer) can now be considered as a collection of rarer tumours. Therefore, rare cancers are
diagnosed in an increasingly large proportion of patients1, and ultimately, all cancers are becoming rare cancers. This
poses a key challenge for clinical trials and research: small patient numbers and wide geographic spread make it
difficult to generate evidence by standard methods2. Recruitment for large clinical trials is complicated and often
impossible3, but traditional registry data do not have the depth needed for detailed subgrouping and analyses.
Consequently, this constitutes a major barrier to research and development in an era of stratified medicine.

There has been growing recognition of the significant role of real-world data (RWD) in addressing the challenges
related to rare cancer research. RWD can be broadly defined as ‘data relating to patient health or experience or care
delivery which is collected outside of clinical trials’4,5. Given that rare cancers are often dispersed across different
healthcare institutions and geographic regions, collating large, high-quality datasets with sufficient depth is complex
when reliant on centralised analysis. Patient privacy and data protection in particular are often the key obstacles in
this setting6. Federated learning (FL), however, enables collaborative analysis without the need to pool data
centrally7,8. Through this approach, individual institutions retain control over their data, ensuring patient privacy and
compliance with data governance regulations. FL with RWD offers a solution to data scarcity for rare cancers,
allowing for better understanding of prognostic factors as well as outcomes in patient sub-groups9,10.

The international atomCAT consortium (Anal cancer Treatment Outcome Modelling with Computer Aided
Theragnostics) was established in 2021 with the aim of demonstrating the value of FL in rare cancers, using anal
cancer as the exemplar. Specifically, the consortium set out to identify prognostic factors for anal cancer using FL11,
building on previous proof-of-concept work12. Anal carcinoma is rare, comprising approximately 0.3% of all cancer
cases13,14. However, its incidence is rising substantially, with studies showing a 35% increase in men and a 75%
increase in women between 1988 and 2012 across Europe15. This trend is largely attributed to increased exposure
to high-risk HPV, following demographic and behavioural shifts since the introduction of the oral contraceptive pill
in the 1960s. Despite the rising incidence, most centres treat relatively few cases per year and may struggle to
identify appropriate evidence-based treatment for patient subgroups, many of whom are under-represented in
clinical trials. As an example, if an oncologist were to see an elderly patient with anal cancer in their clinic today, the
treatment offered might differ substantially across cancer centres and countries16.

Current standard treatment for localised anal cancer consists of concurrent chemotherapy and radiotherapy17–19
delivered with intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT)19. This
approach confers favourable outcomes: modern UK and US multi-centre cohorts have reported three-year overall
survival and disease-free survival rates of 80-85% and 75%, respectively20,21. Historically, most disease relapses have
been reported to occur locoregionally, i.e. at the site of the primary disease or in involved lymph nodes, although
this pattern may be changing with modern treatment strategies21. Previous research has conveyed a clear need for
improved treatment stratification with respect to locoregional treatment intensification or deintensification, as well
as the use of novel agents for systemic disease management. The design of new treatment stratification strategies
depends on robust assessment of risk groups with respect to both locoregional and distant metastatic control22,23.

In this report, we demonstrate the value offered by international real-world curated data research utilising federated
learning through a prospectively planned and pre-registered study of a large international anal cancer patient cohort.
We report on the development and validation of federated prognostic models for overall survival (OS), locoregional
tumour control (LRC), and freedom from distant metastases (FFDM), based on data from anal cancer patients treated
with modern chemoradiotherapy at 16 cancer centres across Europe and Australia.

Results
A total of 1428 patients, treated from 2004 to 2022 across 14 participating centres, were identified for inclusion in
the primary cohort analysis, with an additional 277 patients available for external validation. Table 1 summarises the
patient characteristics of the primary and validation cohorts; with data stratified on individual centres available in
Supplementary Material: Supplementary Note 5. More than 80% of patients were treated between 2010 and 2022.

The overall cohort had a weighted mean age of 62 at the start of radiotherapy, with 28% of patients older than 70
years. The majority of patients (70%) were female. Primary tumour volume varied considerably across centres, from
a mean of 23.9 cm3 in Centre 6 to a mean of 120.5 cm3 in Centre 1. Mean prescribed primary tumour dose (in
equivalent dose in 2Gy per fraction, \\alpha/\\beta=10Gy, EQD2 ) was relatively consistent across centres, ranging from
50.6Gy to 60.1Gy. The most common chemotherapy regimen was Mitomycin C (MMC) and 5-fluorouracil (5FU) /
capecitabine (Cap), which 91% (n=1292) of patients received. Only 6% (n=83) of patients received no chemotherapy.

Summary survival statistics for the three outcomes are provided in Table 2, and presented visually for all centres in
Figure 1, with full per centre data in the Supplementary Material: Supplementary Note 5. Weighted mean potential
follow-up times across the entire cohort were 48.7 months for OS, 42.0 months for LRC, and 41.8 months for FFDM.
At 3 years, the weighted means (range) for OS, LRC, and FFDM were 83% (75%-97%), 83% (73%-90%), and 87% (57%-
96%), respectively. OS rates varied more than LRC and FFDM rates across centres.

The results from the OS, LRC, and FFDM global models, trained using all available data from all primary cohort
centres, are presented in Table 3. Lower T stage, lack of nodal involvement, female sex, younger age, smaller primary
tumour, and use of doublet chemotherapy with MMC or Cisplatin and 5FU or Cap (relative to no chemotherapy)
were associated with better OS. Moreover, lower T stage, smaller primary tumour size, and female sex were
associated with better LRC. Lastly, lack of nodal involvement and smaller primary tumour size were associated with
better FFDM.

The multivariable outcome models allow for individual patient risk prediction using the reference outcome rates
summarised in Supplementary Material: Supplementary Note 5. Observed weighted mean three-year outcome rates
across centres were markedly different between predicted high- and low-risk groups: 73% vs 90% for OS; 76% vs
91% for LRC, and 83% vs 94% for FFDM. Plots for high- and low-risk groups, indicating the observed weighted mean
outcome rates across centres at two, three, and five years are presented in Supplementary Material: Supplementary
Note 5. Figure 2 demonstrates the failure risk distribution for locoregional control and distant metastases for
individual patients in a single centre cohort (Centre 2, n=210); with the impact of tumour volume on failure risk
highlighted.

The global model performance on the primary study cohort was acceptable, with weighted mean c-indices of 0.68,
0.71, 0.69 for OS, LRC, and FFDM, respectively. The performance was stable for the leave-one-centre-out internal
validation (0.67, 0.69, 0.66), indicating very little overfitting. This was confirmed by consistent model performance
in the external validation cohorts (0.72, 0.75, 0.79). Full details of model performance per centre can be found in
Supplementary Material: Supplementary Note 5.

To illustrate the added value of the federated approach, comparator models for all three outcomes were developed
using the same specifications, but only data from a single centre (Centre 2, n=210). When evaluated on the external
validation cohorts, this single-centre models yielded substantially lower C-indices for all outcomes (OS: 0.60, LRC:
0.70, FFDM: 0.62) compared to the federated models (0.72, 0.75, and 0.79, respectively), indicating reduced
generalisability.

None of the secondary models (Supplementary Material: Supplementary Note 5) performed considerably better
than the primary cohort models. For OS and LRC, the secondary models including performance status and treatment
compliance performed minimally better, but at the cost of having additional parameters. This took the total model
parameters beyond the number supported by the available sample size. Using a simple split on staging (high risk: T4
or N+, vs low risk: T1-3 N0), instead of including T stage and N stage individually, was less useful for LRC
(where it became non-significant) but still provided relevant information for OS and FFDM. For distant metastases,
removing GTV size from the model resulted in a significant effect of T stage on outcome, which was not observed
with GTV included. None of the alternative parametrisations of continuous factors (age, GTV size) performed better
than the ones used in the primary models.

Figure 3 presents calibration plots for three-year OS, LRC, and FFDM. Points closer to the reference line indicate
stronger calibration and signify that the predicted outcome rates correspond more closely to actual (observed)
outcome rates. The results indicate that calibration was better for LRC prediction compared to OS and (to a lesser
extent) FFDM. Calibration was generally good in the external validation cohorts (indicated with black circles in Figure
3).

Methods
This study was conducted in accordance with all relevant ethical regulations, with approval and data access
permissions obtained from the HRA and the institutional review boards or equivalent ethics committees at each
participating centre.

INSTITUTIONAL DATA ACCESS & DATA PROTECTION APPROVALS
The atomCAT2 study used FL methodology to develop prognostic models using local datasets from 16 participating
centres (see Supplementary Material: Supplementary Note 2), without exchange of any sensitive individual patient-
level data. Only non-identifiable aggregated information in the form of mathematical parameters, such as model
coefficients, was shared between centres to train and validate the federated models. Each institution obtained
approval from the relevant institutional review board or equivalent ethics committee for accessing and using patient
data for research, and provided a copy of their local data access approval, including approving body and reference
number, to the central study coordinator. For UK centres, central approval was provided by the Health Research
Authority (HRA) (IRAS project ID: 303103, REC reference: 22/WA/0081). Full details of the approving body, approval
reference, and informed consent status for each centre are provided in Supplementary Note 2.

The atomCAT2 study involved no protocol-specific intervention beyond standard-of-care radiotherapy. Given the
retrospective, non-interventional nature of the study and the use of routinely collected, de-identified data that
remained at the originating institutions, the requirement for individual patient informed consent was waived or
deemed not required by the approving bodies, as detailed in Supplementary Note 2.

PROSPECTIVE STUDY PROTOCOL & STATISTICAL ANALYSIS PLAN
A prospective study protocol and comprehensive statistical analysis plan were developed collaboratively,
published11, and registered in Open Science Foundation48. Details of data item selection and definition, outcome
definition, model specification, sample size calculation, and model evaluation are available in the protocol11. Model
development, validation procedure and results were reported in accordance with the TRIPOD statement and
checklist49 (see Supplementary Material: Supplementary Note 1). Figure 4 illustrates the workflow for the entire
study, including previously published preliminary and preparatory work12.

STUDY DESIGN & PATIENT POPULATION
Patients treated with radical intent external beam radiotherapy for primary anal squamous cell carcinoma, with or
without concomitant chemotherapy, were included. Inclusion was restricted to patients treated with conformal
radiotherapy techniques (3D conformal radiotherapy (3D-CRT) or IMRT/VMAT). Individuals treated with palliative
intent, and patients who had received prior pelvic radiotherapy or brachytherapy (either primary or as boost
treatment) were excluded. Patients were treated according to each participating centre’s protocols, which consisted
of concurrent radiotherapy and chemotherapy with varying regimens, or radiotherapy only.

OUTCOME DEFINITIONS
Three outcomes were employed: OS, LRC, and FFDM. The Core Outcome Research Measures in Anal Cancer
(CORMAC) initiative has identified these as key outcome measures for anal cancer research50. Complete definitions
for all three outcomes can be found in the study protocol11, in brief: OS included death from any event, with
censoring at last clinical follow-up; LRC events included lack of complete response at evaluation following
chemoradiotherapy and locoregional disease progression during follow-up. Patients were censored at death, at last
clinical follow-up, if undergoing abdominoperineal resection for non-disease related reasons, or in case of distant
metastases; distant metastasis was defined as disease recurrence outside of the primarily treated volumes, including
any non-irradiated nodes outside the pelvis or other organs e.g. lung or liver. Patients were censored at local
recurrence, at death, or at last clinical follow-up. All times were calculated from first radiotherapy treatment date.

IDENTIFICATION OF RELEVANT PROGNOSTIC FACTORS
To identify potential prognostic factors for the three outcomes, a systematic review of the literature was
conducted24; this included studies published after 2000, with medium-to-large cohorts (>100 patients), reporting
on anal cancer outcomes after treatment with conformal radiotherapy. Factors identified as prognostic through
multivariable analysis in several studies were selected and prioritised (based on the number of studies reporting on
them), which formed an initial list of relevant data to be collected. This list was reviewed by three senior clinical
oncologists (AG, MGG, MB), who added additional relevant factors. A data scoping exercise across participating
centres limited the list to factors consistently available in routine clinical data. HIV and HPV status, though clinically
relevant, were excluded due to inconsistent availability and high risk of informative missingness across participating
centres. Finally, a prioritised list of factors for each prognostic model (see Sample Size section below) was created
by the senior oncologist team. A full description of the variable selection and correlation assessment process,
including clinical rationale and literature sources, is provided in the published protocol11. A data dictionary was
created and shared between all centres for standardised data collection, formatting, and reporting. Additional detail
on missing data handling and standardisation procedures across centres is provided in Supplementary Material:
Supplementary Note 3.

PATIENT DATA COLLECTION & MISSING DATA
Patient data were identified and extracted from existing research and clinical databases at each participating site.
To ensure good data quality, each institution spot checked all extracted data, ensuring adherence to the coding
system specified in the data dictionary and identifying any outliers. The study protocol11 pre-specified a framework
on how to deal with missing data at individual centres, in multiple different scenarios, including use of data
imputation. Only gross tumour volume (GTV, cm3) required use of imputation in two centre datasets; this was
implemented based on a global median of means from all other centres.

SAMPLE SIZE
A prospective sample size calculation was performed using the framework devised by Riley et al.39, and implemented
using the “pmsampsize” package in R, to determine the minimum sample size required to fit a Cox proportional
hazards model for each of the three outcomes. The number of prognostic factors included in the final models was
based on the total number of patients available in the primary consortium cohort; with the specific set of factors
based on the prioritised list developed from a systematic literature review and expert oncologist input. The detailed
methodology used to calculate sample size and prioritised list of factors is provided in the study protocol11.

DESCRIPTIVE DATA ANALYSIS
Descriptive data analysis included calculation of summary and survival statistics from each centre. Overall summary
statistics for continuous variables were calculated using weighted means. Two-year, three-year, and five-year OS,
LRC, and FFDM were estimated by Kaplan-Meier methods. Potential follow-up times were based on the inverse
Kaplan-Meier estimator51.

MODEL DEVELOPMENT & SPECIFICATION
Primary analysis consisted of development and internal validation (TRIPOD Type 2b52) of federated multivariable Cox
Proportional Hazards models31 across 14 centres; separately for OS, LRC, and FFDM. Secondarily, external validation
(TRIPOD Type 3) was performed using previously unseen data from two additional institutions (see below). Based
on available patient numbers in the 14 primary cohort centres (n=1428), eight parameters could be included in the
models. Predictors used were: age, biological sex, T stage, nodal involvement, primary GTV (using a log10
transformation), prescribed primary tumour dose (in EQD2 \\alpha/\\beta=10Gy ), and histology (as per the prioritisation lists in the
study protocol). Biological sex was included as a predictor in all multivariable models and was recorded based on
clinical documentation at each participating centre. Gender identity was not separately collected or verified. Sex-
specific analyses beyond inclusion as a covariate were not pre-specified and were not performed.

A set of secondary models were additionally developed to explore the impact of different factor parametrisation
(e.g. for age and GTV), different definitions (e.g. for tumour staging), and including secondary factors only available
for a subset of patients (e.g. performance status). All analyses were run as multivariable models, with no data driven
feature selection or model reduction. Patients with metastatic disease at baseline were excluded from all models
for FFDM. An additional exploratory comparison between the federated models and a single-centre model (Centre
2) was performed to illustrate the added value of the federated approach.

FEDERATED MODEL LEARNING
Description of the FL architecture, as well as further technical details of the model training algorithm’s aggregation
process, convergence thresholds, and iteration scheme are provided in Supplementary Material: Supplementary
Note 4. In brief, models were trained iteratively through exchange of local model coefficients and fit errors between
each participating centre and a central server. At each iteration, the central server aggregated model coefficients
computed locally in each centre and a single globally-convergent model was determined by minimising the total
error53–55. The process was repeated until pre-specified convergence criteria were fulfilled. The source code for
infrastructure implementation and the federated Cox regression algorithm utilised for all prognostic models are
freely accessible on GitHub (see Supplementary Material: Supplementary Note 4).

MODEL REPORTING
For each model developed, the estimated factor effects were reported as Hazard Ratios (HR), along with 95%
confidence intervals (CI). Factors were deemed prognostic if their 95% CI did not overlap with 1. For each of the
three outcomes, reference outcome rates at 2 years, 3 years and 5 years were calculated. The reference outcome
rate can be defined as the outcome rate when all model factors are set to their baseline value. To calculate reference
outcome rates, all categorical factors were set to 0, age at the start of radiotherapy was set to 35 years, prescribed
dose to the primary tumour was set to 40 EQD2 \\alpha/\\beta=10Gy , and log of GTV was set to 0.02572 (GTV of 1.1 cm3). The
combination of reference outcome rates and factor effect estimates (HR) enables prediction of outcomes for future
patients, rendering the models useable for individual patient outcome prediction.

EVALUATION AND VISUALISATION OF MODEL PERFORMANCE
Model performance was assessed using Harrell’s concordance index (c-index, providing a measure of model
discrimination)56 on a per-centre basis, and a global weighted average calculated. A closed-loop internal-external
“leave-one-centre-out” cross-validation method57 was applied to obtain the out-of-sample performance for the
primary study cohort, providing an initial estimate of the over-optimism of the global model. The weighted mean of
c-index values generated in all centres was reported. For model calibration, predicted three-year OS, LRC, and FFDM
rates for each patient were calculated based on the primary models, and patients were allocated to ‘high risk’ or
‘low risk’ groups according to the global predicted mean for each outcome. For each centre, the actual (observed)
three-year outcome rates for ‘high risk’ and ‘low risk’ patients were evaluated using Kaplan-Meier estimates and
subsequently plotted against the mean predicted outcome rates for each group to indicate model calibration. Finally,
summary plots were generated to illustrate the observed weighted mean outcome rates for predicted ‘high risk’ and
‘low risk’ groups across centres at two, three, and five years.

EXTERNAL VALIDATION
The models developed in the primary consortium cohort were further evaluated for their calibration and
discrimination ability in two external validation cohorts. The external validation centres were selected based on
cohort size, completeness of routinely collected variables, and geographical representation (one European and one
Australian centre). Both were independent from the 14 training centres to ensure external validation across distinct
populations. This process aimed to assess the reproducibility and generalisability of the models. Patient data
collection and formatting followed the same study protocol as for the primary consortium cohort, and analyses
retained the principles of no individual patient-level data sharing.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study provides much-needed data on a rare cancer type, addressing a critical gap in oncology research and setting a benchmark for similar studies. It explores the use of Federated Learning (FL) to develop and validate prognostic models for anal cancer outcomes, leveraging real-world data from multiple international centers. The primary objective is to address the challenges of data scarcity and privacy concerns in rare cancer research by utilizing FL, which allows collaborative model training without sharing individual patient data. With data from 16 centers across Europe and Australia, this study represents one of the largest international cohorts of anal cancer patients. The geographic diversity adds to the generalizability of its findings.\n\nThe study included baseline patient and treatment characteristics data, and the development of federated multivariable Cox Proportional Hazards models for overall survival (OS), locoregional control (LRC), and freedom from distant metastases (FFDM). The models demonstrated good performance and calibration during cross-validation and external validation, with weighted c-indices of 0.68, 0.71, and 0.69 for OS, LRC, and FFDM, respectively.\n\nThe prospective planning, including sample size calculation and study pre-registration, highlights the rigor and reliability of the research design. The adherence to the TRIPOD guidelines further underscores the study's commitment to transparency and reproducibility. However there are some points needs to be highlighted :\nThe federated learning architecture had limitations in implementing standard prognostic model development steps like feature selection, advanced methods for missing data imputation, and correlation analysis.\nThe introduction/background effectively sets the stage but might benefit from a more compelling hook—perhaps a striking statistic or anecdote to draw the reader in. Similarly, a workflow to introduce the methodology could have presented a better demonstration about the whole study.\nThe models were validated both internally (using cross-validation) and externally (via independent datasets), ensuring the results are robust and less likely to be overfitted to the training data. The study addresses a critical gap, setting a benchmark for similar studies and the article discusses limitations, it could explicitly suggest how future studies might address these gaps. For instance, could emerging technologies improve federated learning methodologies.\nThe identification of prognostic factors like tumor stage, nodal involvement, and tumor volume provides actionable insights for clinical decision-making. These findings lend weight to the conclusion that FL can uncover critical prognostic information to inform personalized treatment strategies.\nThe study’s conclusion touches on potential applications, but this could be further elaborated. Offering more examples of how the findings could influence clinical practice or inform policy might strengthen the impact.\nTables and figures are used well, but their clarity might be enhanced. For example, ensuring all figures have detailed captions and are self-explanatory could aid comprehension\nThere are many specialized terms and acronyms (e.g., OS, LRC, FFDM). Adding a glossary intended for wider dissemination could make the article more reader-friendly.", "reviewer_2": "Overall, the manuscript presents an innovative federated learning approach to develop prognostic models based on a large, international, multi-center cohort of anal cancer patients. This approach holds promise for enhancing risk stratification in rare cancers by overcoming data privacy challenges and integrating heterogeneous data. However, several issues merit attention to strengthen the manuscript:\nMajor Issues:\n1. The authors only presented the survival curves of all patients, but failed to compare the survival curves between the high - risk and low - risk groups predicted by the model. This is likely the most crucial result in a prognosis - related article, as it directly demonstrates the discriminatory power of the prognostic model for patient survival.\n2. There is a lack of comparative analysis between the federated learning approach and simpler, single-center trained model or traditional clinical stratification methods (e.g., TNM stage), which limits the demonstration of the method’s added value.\n3. Table 3 only shows the results of univariate analysis, while lacking multivariate analysis to adjust for confounding factors. Multivariate analysis is essential to identify the independent prognostic factors and strengthen the reliability of the research findings.\nMinor Issues:\n1. The federated learning modeling in this study is conducted for a simple statistical model like Cox regression. It is unclear whether this training mode would still be applicable if other machine learning models or deep learning models were used. The authors are expected to discuss the potential challenges and feasibility when applying the federated learning approach to different types of models.\n2. Some methodological details, such as the handling of missing data and standardization procedures across centers, are briefly described and would benefit from further elaboration.", "reviewer_3": "I appreciate the opportunity to review the manuscript entitled \"Federated Learning with Real-World Data: An International Multi-Centre Study to Develop and Validate Prognostic Models for Anal Cancer.\"\n\nThe authors present an innovative approach to developing prognostic models for rare cancers through federated learning, which enables collaborative analysis without sharing patient-level data—only model parameters are exchanged between centers. This strategy effectively addresses data privacy concerns and regulatory challenges.\n\nFrom a clinical standpoint, this study is noteworthy. Its strengths include a robust multicenter design and an unusually large cohort for such a rare malignancy.\n\nI would like to raise the following questions for the authors:\n1. While the federated learning approach offers clear advantages regarding data protection and decentralization, could the authors elaborate on whether results would differ significantly if a traditional centralized model had been used? If available, a brief comparison or discussion on this point would enhance the manuscript.\n2. Was HIV status included in the dataset? Although I understand that HPV status may often be unavailable, HIV infection is highly relevant in the context of anal cancer and is commonly recorded in clinical settings. If not included, please comment on this limitation.\n3. Was there any available clinical information regarding prior anal condyloma disease? While indirect, this could serve as a surrogate marker for HPV infection and is usually easier to extract from medical records.\n4. In your view, could these prognostic models influence or change current clinical decision-making for patients with anal cancer? If so, how might they be integrated into routine practice?\n\nOnce again, I thank you for the opportunity to review this important and well-executed study."}, "author_response": "Dear Reviewer 1,\nThank you for the reply and your thorough review of the manuscript. We very\nmuch appreciate your input and help on improving the manuscript. We have\naddressed your comments below.\n[...]\nDear Reviewer 2,\nThank you for the constructive and useful comments on this manuscript. We\nappreciate the review and believe that your suggestions have contributed\npositively to the manuscript and have improved the content significantly. Replies\nto individual comments are provided below.\n[...]\nDear Reviewer 3,\nThe authors would like to thank you for the review and for taking the time to help\nus improve the manuscript. We have addressed your comments below."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Authors have addressed all the concerns, and the article can be accepted in its current form.", "reviewer_2": "Thank you for your detailed clarifications. All of my previous concerns have now been satisfactorily addressed.", "reviewer_4": "Major Comments\nFederated Learning Methodology: Explanation of FL is minimal. Readers unfamiliar with the concept may not understand how it works.\nSuggestion: Include a concise description of FL, aggregation algorithm (e.g., FedAvg, FedProx), number of iterations, convergence thresholds, error minimization process, handling of site-level variability, and correlation diagnostics or variable selection criteria for model stability.\nExternal Validation Centers: Selection criteria for the two external centers are unclear.\nSuggestion: Clarify how these centers were chosen to ensure representativeness and independence from the primary cohort.\nCohort Differences and Statistical Assessment: Clinically meaningful differences exist between primary and validation cohorts (sex, T stage, tumor volume, chemotherapy regimens).\nSuggestion: Include formal statistical tests (chi-square, Fisher’s exact, t-test, Mann-Whitney) to assess baseline differences and discuss potential impact on model performance and generalizability.\nClinical Relevance of Discrimination Metrics: C-indices are modest (OS: 0.68–0.72, FFDM: 0.69–0.79).\nSuggestion: Discuss clinical implications and limitations of these discrimination metrics.\nSingle-Center Comparison\nFederated learning is compared to a single-center model, but statistical support for improved generalizability is lacking.\nSuggestion: Include confidence intervals or statistical testing to strengthen this claim.\nVariable Selection and Model Parameters: Rationale for including/excluding variables (performance status, treatment compliance, GTV, staging) is unclear, particularly regarding trade-offs with sample size.\nSuggestion: Clarify decision process and limitations.\nRadiotherapy Dose: Claim that prescribed dose is not prognostic contradicts prior literature.\nSuggestion: Discuss possible confounding, cohort heterogeneity, or data limitations.\nClinical Novelty: Most identified prognostic factors (T stage, N stage, sex, GTV) are already known.\nSuggestion: Clarify added clinical value beyond demonstrating FL methodology.\nMinor Comments\nKaplan-Meier Analyses: Aggregated survival curves do not stratify by key clinical variables.\nSuggestion: Consider subgroup KM curves (T/N stage, treatment regimen).\nFollow-Up Data: Centres 6 and 14 excluded due to missing 5-year follow-up.\nSuggestion: Acknowledge as potential source of bias for long-term predictions.\nTreatment Completion and Response Assessment: Proportion completing planned RT/chemotherapy, timing, criteria for response, and classification of relapses are not reported.\nSuggestion: Include these details to contextualize outcomes.\nCalibration Interpretation: OS and FFDM calibration slightly worse than LRC; implications not discussed. Clarify how imperfect calibration affects clinical use.\nSubgroup Analysis: Model performance in subgroups (elderly, high-risk T/N stage) not reported.\nSuggestion: Include subgroup performance or discuss limitations for clinical decision-making.\nComparison with Centralized Models\nMathematical equivalence mentioned, but no empirical comparison. Clarify limitations of this assumption for real-world performance.\nMissing Variables:HIV, HPV, and performance status excluded due to missing data; impact on confounding and generalizability is unclear. Discuss how this may bias results.\nSpeculative Future Applications:\nDiscussion of AI, LLMs, multimodal data is speculative. Clearly separate forward-looking statements from confirmed study results. Provide concrete guidance or scenarios for clinical application.\nFormatting Consistency: The manuscript alternates between “centre” and “center.” Standardize spelling throughout."}, "author_response": "Dear Reviewer #4,\nThank you for the detailed review of the manuscript and the constructive feedback.\nBelow, we provide a point-by-point response to your comments. Where appropriate, we\nindicate proposed clarifications or edits included in the manuscript."}]

Decision letter