Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study

Shiqian Zhang; Ge Zhang; Ming Wang; Song-Bin Guo; Fuqi Wang; Yun Li; Kaisaierjiang Kadier; Zhaokai Zhou; Pengpeng Zhang; Hao Chi; Chuchu Zhang; Quanbo Zhou; Pin Lyu; Shuaiya Zhao; Shuaixi Yang; Weitang Yuan

doi:10.1200/po.24.00089

Article · 2024 Deep GEO

Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study

JCO Precision Oncology

Full Authors

Shiqian Zhang
Ge Zhang
Ming Wang
Song-Bin Guo
Fuqi Wang
Yun Li
Kaisaierjiang Kadier
Zhaokai Zhou
Pengpeng Zhang
Hao Chi
Chuchu Zhang
Quanbo Zhou
Pin Lyu
Shuaiya Zhao
Shuaixi Yang
Weitang Yuan

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Shiqian Zhang, Ge Zhang, Ming Wang, Song-Bin Guo, Fuqi Wang, Yun Li, Kaisaierjiang Kadier, Zhaokai Zhou, Pengpeng Zhang, Hao Chi, Chuchu Zhang, Quanbo Zhou, Pin Lyu, Shuaiya Zhao, Shuaixi Yang, Weitang Yuan. Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study. JCO Precision Oncology. 2024;8:e2400089. https://doi.org/10.1200/po.24.00089

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Research & review context

Research fields: Artificial intelligence in oncology
Rectal cancer prognosis
Review topics: Machine learning for postoperative survival
Interpretable cancer risk stratification
Scientific role: Interpretable survival-modeling framework for rectal-cancer patients after robot-assisted proctectomy.
Evidence position: Two-center retrospective human prognostic modeling with external evaluation; not prospective clinical utility or a causal survival advantage of robotic surgery.
Related concepts: Postoperative prognostic modeling
Externally evaluated survival models
Possible citation contexts: Methods: interpretable AI for postoperative oncology prognosis.
Evidence synthesis: survival models in the robot-assisted rectal-cancer surgery population, retaining external-performance limitations.

Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.

Questions & evidence · Evidence limits · Original article & citation downloads

Evidence Snapshot

Artificial Intelligence Hybrid Survival Assessment System for Robot-Assisted Proctectomy: A Retrospective Cohort Study

RAP-AIscore is an interpretable postoperative overall-survival model developed in a retrospective two-center study of 506 patients undergoing robot-assisted proctectomy, with internal validation and independent retrospective SYEMH evaluation.

Unique total: n=506
NSCVRCS Discovery: n=268; subset of NSCVRCS Meta
NSCVRCS Replication: n=116; subset of NSCVRCS Meta
NSCVRCS Meta: n=384
SYEMH: n=122
Initial predictors: 53
Algorithms: 12
Modeling schemes: 132
Final predictors: 10
average C-index: 0.833

Research Question

Can a systematically selected hybrid survival-machine-learning framework provide interpretable and externally validated postoperative overall-survival risk stratification for patients with rectal cancer undergoing robot-assisted proctectomy?

Author Evidence Summary

This retrospective two-center study evaluated 506 unique patients undergoing robot-assisted proctectomy for rectal cancer. The NSCVRCS Meta cohort included 384 patients divided into development and internal-validation subsets; an independent SYEMH cohort included 122 patients. A comparison of 132 candidate survival-modeling schemes selected Surv.coxboost + Surv.aorsf and yielded an interpretable model with 10 predictors. RAP-AIscore showed internal time-dependent survival discrimination and separated patients into three prognostic states. Independent SYEMH evaluation was retrospective: its 1-year AUC was 0.587, with 2- and 3-year AUCs of 0.754 and 0.810. Prospective clinical utility and improved survival from model-guided care remain unestablished.

Key Findings

KF1 The study constructed RAP-AIscore using a systematic comparison of 132 candidate survival-modeling schemes in a double-center retrospective cohort.

506 unique RAP patients; NSCVRCS Meta n=384 plus independent SYEMH n=122. Discovery and Replication are subsets of Meta and must not be counted twice.

Evidence for KF1
Measure	Value
total unique patients	506
NSCVRCS Meta	384
Discovery	268
Replication	116
SYEMH	122
modeling schemes	132

Source locator: Abstract: Methods; Methods: Data Sources and Study Population; Results: Outcome and Treatment of Missing Values

Permalink to KF1

KF2 The selected Surv.coxboost + Surv.aorsf hybrid scheme achieved the highest average C-index among the 132 evaluated schemes.

Systematic retrospective comparison of candidate survival-modeling schemes.

average C-index: 0.833

Source locator: Results: Comprehensive Development of AI-Hybrid Score System; Fig. 2B-D

Permalink to KF2

KF3 RAP-AIscore showed time-dependent postoperative overall-survival discrimination in the internal NSCVRCS cohorts.

Retrospective internal development and validation; Meta contains the Discovery and Replication subsets.

Evidence for KF3
Measure	Value
Replication 12/36/60/72-month AUCs	0.851 / 0.849 / 0.902 / 0.915
Meta 12/36/60/72-month AUCs	0.911 / 0.924 / 0.931 / 0.948
Discovery 12/36/60/72-month AUCs	0.927 / 0.960 / 0.949 / 0.962

Source locator: Results: The Predictive Value of RAP-AIscore System; Fig. 2E-H; Data Supplement Fig. S6

Permalink to KF3

KF4 The final interpretable RAP-AIscore used 10 accessible predictors.

Predictive model variables; their inclusion does not establish causal effects on mortality.

Evidence for KF4
Measure	Value
predictor count	10
predictors	pTNM, ROM, CEA, Ns, PostCEA, Ts, CA199, HLP, Postchem, Minutes

Source locator: Fig. 3C-D; Data Supplement Table S5

Permalink to KF4

KF5 Conditional-inference analysis separated patients into three prognostic risk states.

The Results text reports Replication low/intermediate/high counts of 68/20/19, which sum to 107 and do not reconcile with the stated Replication cohort size of 116. These subgroup counts are retained as article-reported values; the denominator discrepancy is unresolved and must not be interpreted as documented exclusions or a complete partition of all 116 patients.

Evidence for KF5
Measure	Value
low	RAP-AIscore <= 2.394
intermediate	> 2.394 to <= 5.55
high	> 5.55
Article-reported Replication low/intermediate/high counts (sum 107; cohort 116; unresolved denominator discrepancy)	68 / 20 / 19
Meta low/intermediate/high counts	258 / 60 / 66
three-state discrimination	P < 0.001 in Replication and Meta

Source locator: Results: Interpretability and Clinical Stratification of RAP-AIscore; Fig. 4A-E

Permalink to KF5

KF6 RAP-AIscore retained prognostic discrimination in the independent SYEMH external cohort, although early external discrimination was modest.

Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.

Evidence for KF6
Measure	Value
1-year AUC	0.587
2-year AUC	0.754
3-year AUC	0.810
high-vs-low survival	P < 0.0001
three-state discrimination	P < 0.001

Source locator: Results: Extension to External Cohort From the RAP-AIscore; Fig. 5A-J

Permalink to KF6

Study Design & Model Development

Study profile

Study Design: Double-center retrospective cohort study with internal and independent external validation
Evidence Type: Human postoperative prognostic survival-modeling study
Unique total: 506
Population: Patients with rectal cancer undergoing robot-assisted proctectomy
Primary Endpoint: In-hospital and post-discharge overall survival
Secondary Endpoint: Postoperative prognostic risk stratification and time-dependent survival discrimination
External validation: Yes

Cohort hierarchy

Cohort	Role	n	Relationship
NSCVRCS Discovery	model development	268	Subset of NSCVRCS Meta
NSCVRCS Replication	internal validation	116	Subset of NSCVRCS Meta
NSCVRCS Meta	complete in-house/modeling cohort	384	Contains NSCVRCS Discovery, NSCVRCS Replication
SYEMH	independent external validation	122	Independent cohort

Data modalities

electronic health records
clinical and laboratory variables
operative and pathologic characteristics
longitudinal survival outcomes

Model development

Initial predictors: 53
Algorithms: 12
Modeling schemes: 132
Selected algorithms: Surv.coxboost, Surv.aorsf
Feature selection: Filter + sequential-forward-floating wrapper feature selection maximizing Harrell C-index
Final predictors: 10
Validation Strategy: 10 repetitions of 10-fold cross-validation, Monte Carlo cross-validation: 100 iterations, sampling rate 0.7, bootstrap: 1,000 iterations, sampling rate 0.7

Final predictor set

pTNM
ROM
CEA
Ns
PostCEA
Ts
CA199
HLP
Postchem
Minutes

Interpretability

SHAP
nomogram
conditional inference tree
t-SNE risk-state visualization

What This Study Adds

Retrospective development of an interpretable survival model for patients undergoing robot-assisted proctectomy.
Systematic comparison of hybrid survival-modeling schemes.
Internal time-dependent discrimination and independent retrospective external evaluation.
Three-state postoperative prognostic stratification.

Evidence Scope

Supports

Retrospective development of an interpretable RAP-specific survival model.
Systematic hybrid model selection.
Internal time-dependent survival discrimination.
Independent retrospective external evaluation, with modest 1-year AUC of 0.587.
Three-state prognostic stratification.

Does Not Establish

Prospective clinical utility.
Improved survival from RAP-AIscore-guided care or treatment efficacy.
Causal effects of the 10 predictors.
Universal generalizability to all RAP populations or settings.
Replacement of clinician judgment or pathologic staging.

Limitations

The two-center study and independent external evaluation were retrospective.
Prospective clinical utility and survival benefit from model-guided management were not established.
External 1-year discrimination was modest, with AUC 0.587.
Generalizability to all hospitals and RAP populations was not established.
The Results text reports Replication low/intermediate/high counts of 68/20/19, which sum to 107 and do not reconcile with the stated Replication cohort size of 116. These subgroup counts are retained as article-reported values; the denominator discrepancy is unresolved and must not be interpreted as documented exclusions or a complete partition of all 116 patients.

When This Study Is Useful to Cite

CU1: Which studies developed and externally evaluated survival models specifically for patients undergoing robot-assisted proctectomy?
The study constructed RAP-AIscore using a systematic comparison of 132 candidate survival-modeling schemes in a double-center retrospective cohort. 506 unique RAP patients; NSCVRCS Meta n=384 plus independent SYEMH n=122. Discovery and Replication are subsets of Meta and must not be counted twice. RAP-AIscore retained prognostic discrimination in the independent SYEMH external cohort, although early external discrimination was modest. Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.
Evidence: KF1, KF6
CU2: How can systematic hybrid model selection be applied to postoperative survival prediction?
The study constructed RAP-AIscore using a systematic comparison of 132 candidate survival-modeling schemes in a double-center retrospective cohort. 506 unique RAP patients; NSCVRCS Meta n=384 plus independent SYEMH n=122. Discovery and Replication are subsets of Meta and must not be counted twice. The selected Surv.coxboost + Surv.aorsf hybrid scheme achieved the highest average C-index among the 132 evaluated schemes. Systematic retrospective comparison of candidate survival-modeling schemes.
Evidence: KF1, KF2
CU3: What evidence supports time-dependent survival discrimination by RAP-AIscore?
RAP-AIscore showed time-dependent postoperative overall-survival discrimination in the internal NSCVRCS cohorts. Retrospective internal development and validation; Meta contains the Discovery and Replication subsets. RAP-AIscore retained prognostic discrimination in the independent SYEMH external cohort, although early external discrimination was modest. Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.
Evidence: KF3, KF6
CU4: Which clinical variables were incorporated into the interpretable 10-predictor RAP-AIscore?
The final interpretable RAP-AIscore used 10 accessible predictors. Predictive model variables; their inclusion does not establish causal effects on mortality.
Evidence: KF4
CU5: How did RAP-AIscore define low, intermediate and high postoperative prognostic states?
Conditional-inference analysis separated patients into three prognostic risk states. The Results text reports Replication low/intermediate/high counts of 68/20/19, which sum to 107 and do not reconcile with the stated Replication cohort size of 116. These subgroup counts are retained as article-reported values; the denominator discrepancy is unresolved and must not be interpreted as documented exclusions or a complete partition of all 116 patients.
Evidence: KF5
CU6: Was RAP-AIscore independently externally validated?
RAP-AIscore retained prognostic discrimination in the independent SYEMH external cohort, although early external discrimination was modest. Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.
Evidence: KF6

What This Study Should Not Be Cited to Claim

RAP-AIscore-guided management improves patient survival.
Prospective clinical utility has been established.
The 10 predictors are causal determinants of mortality.
Performance generalizes to every hospital or population.
RAP-AIscore replaces standard clinical or pathological assessment.

Evidence Matrix

Row ID	Component	Context	Finding	Evidence Level	Scope	Source Locator	Evidence refs
evidence-rap-aiscore-1	KF1	506 unique RAP patients; NSCVRCS Meta n=384 plus independent SYEMH n=122. Discovery and Replication are subsets of Meta and must not be counted twice.	The study constructed RAP-AIscore using a systematic comparison of 132 candidate survival-modeling schemes in a double-center retrospective cohort. total unique patients: 506; NSCVRCS Meta: 384; Discovery: 268; Replication: 116; SYEMH: 122; modeling schemes: 132	Retrospective survival modeling	506 unique RAP patients; NSCVRCS Meta n=384 plus independent SYEMH n=122. Discovery and Replication are subsets of Meta and must not be counted twice.	Abstract: Methods; Methods: Data Sources and Study Population; Results: Outcome and Treatment of Missing Values	KF1
evidence-rap-aiscore-2	KF2	Systematic retrospective comparison of candidate survival-modeling schemes.	The selected Surv.coxboost + Surv.aorsf hybrid scheme achieved the highest average C-index among the 132 evaluated schemes. average C-index: 0.833	Retrospective survival modeling	Systematic retrospective comparison of candidate survival-modeling schemes.	Results: Comprehensive Development of AI-Hybrid Score System; Fig. 2B-D	KF2
evidence-rap-aiscore-3	KF3	Retrospective internal development and validation; Meta contains the Discovery and Replication subsets.	RAP-AIscore showed time-dependent postoperative overall-survival discrimination in the internal NSCVRCS cohorts. Replication 12/36/60/72-month AUCs: 0.851 / 0.849 / 0.902 / 0.915; Meta 12/36/60/72-month AUCs: 0.911 / 0.924 / 0.931 / 0.948; Discovery 12/36/60/72-month AUCs: 0.927 / 0.960 / 0.949 / 0.962	Retrospective survival modeling	Retrospective internal development and validation; Meta contains the Discovery and Replication subsets.	Results: The Predictive Value of RAP-AIscore System; Fig. 2E-H; Data Supplement Fig. S6	KF3
evidence-rap-aiscore-4	KF4	Predictive model variables; their inclusion does not establish causal effects on mortality.	The final interpretable RAP-AIscore used 10 accessible predictors. predictor count: 10; predictors: pTNM, ROM, CEA, Ns, PostCEA, Ts, CA199, HLP, Postchem, Minutes	Retrospective survival modeling	Predictive model variables; their inclusion does not establish causal effects on mortality.	Fig. 3C-D; Data Supplement Table S5	KF4
evidence-rap-aiscore-5	KF5	The Results text reports Replication low/intermediate/high counts of 68/20/19, which sum to 107 and do not reconcile with the stated Replication cohort size of 116. These subgroup counts are retained as article-reported values; the denominator discrepancy is unresolved and must not be interpreted as documented exclusions or a complete partition of all 116 patients.	Conditional-inference analysis separated patients into three prognostic risk states. low: RAP-AIscore <= 2.394; intermediate: > 2.394 to <= 5.55; high: > 5.55; Article-reported Replication low/intermediate/high counts (sum 107; cohort 116; unresolved denominator discrepancy): 68 / 20 / 19; Meta low/intermediate/high counts: 258 / 60 / 66; three-state discrimination: P < 0.001 in Replication and Meta	Retrospective survival modeling	The Results text reports Replication low/intermediate/high counts of 68/20/19, which sum to 107 and do not reconcile with the stated Replication cohort size of 116. These subgroup counts are retained as article-reported values; the denominator discrepancy is unresolved and must not be interpreted as documented exclusions or a complete partition of all 116 patients.	Results: Interpretability and Clinical Stratification of RAP-AIscore; Fig. 4A-E	KF5
evidence-rap-aiscore-6	KF6	Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.	RAP-AIscore retained prognostic discrimination in the independent SYEMH external cohort, although early external discrimination was modest. 1-year AUC: 0.587; 2-year AUC: 0.754; 3-year AUC: 0.810; high-vs-low survival: P < 0.0001; three-state discrimination: P < 0.001	Independent retrospective external evaluation	Independent retrospective SYEMH evaluation, n=122; external 1-year AUC was 0.587.	Results: Extension to External Cohort From the RAP-AIscore; Fig. 5A-J	KF6

Q&A

What population was RAP-AIscore developed for?

Patients with rectal cancer undergoing robot-assisted proctectomy; the study modeled postoperative overall survival retrospectively.

Evidence: KF1

How many independent patients and cohorts were studied?

There were 506 unique patients: 384 in NSCVRCS Meta and 122 in independent SYEMH. NSCVRCS Discovery n=268 and Replication n=116 are subsets of Meta, not additional patients.

Evidence: KF1

How were 132 modeling schemes compared?

The study systematically compared candidate survival-modeling schemes and selected Surv.coxboost + Surv.aorsf, which achieved the highest average C-index, 0.833.

Evidence: KF1, KF2

What performance was observed internally?

At 12/36/60/72 months, AUCs were 0.851/0.849/0.902/0.915 in Replication, 0.911/0.924/0.931/0.948 in Meta, and 0.927/0.960/0.949/0.962 in Discovery. These are retrospective time-dependent discrimination results.

Evidence: KF3

What happened in independent SYEMH validation?

In the independent retrospective cohort, 1-/2-/3-year AUCs were 0.587/0.754/0.810. Early discrimination was modest. High-versus-low survival differed at P < 0.0001 and three-state discrimination at P < 0.001.

Evidence: KF6

Does RAP-AIscore have proven prospective clinical benefit?

No. The retrospective model-development, internal-validation and external-evaluation findings do not establish prospective clinical utility or survival benefit from model-guided care.

Evidence: KF1, KF3, KF6

Concepts & Entities

Conditions

rectal cancer

Procedures

robot-assisted proctectomy

Methods

survival machine learninghybrid model selectionSHAPconditional inference tree

Outcomes

overall survivalpostoperative risk stratification

Validation

internal validationindependent external validation

Related Research

AI hybrid survival assessment for advanced heart failure patients with renal dysfunction
Related hybrid survival-machine-learning framework developed by the same research program in a different clinical population; AIHFLevel addresses advanced heart failure with renal dysfunction, whereas RAP-AIscore addresses postoperative survival after robot-assisted proctectomy. The models do not validate one another. · DOI: 10.1038/s41467-024-50415-9

Publication & Provenance

DOI: https://doi.org/10.1200/po.24.00089
Doi Url: https://doi.org/10.1200/po.24.00089
Publisher: https://ascopubs.org/doi/10.1200/PO.24.00089
PubMed: https://pubmed.ncbi.nlm.nih.gov/39432882/

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Reply to: Addressing Bias in Feature Importance: A Hybrid Approach for Risk Prediction in Prognostic Survival Models
Relationship stated on that evidence page: The original retrospective robot-assisted proctectomy study discussed in the reply; consult it for patient counts and model-performance evidence.
Efficacy and Safety of Apatinib Treatment for Advanced Cholangiocarcinoma After Failed Gemcitabine-Based Chemotherapy: An Open-Label Phase II Prospective Study
Relationship stated on that evidence page: Related clinical oncology research illustrating a different study design: retrospective postoperative risk modeling rather than a single-arm drug trial. It does not validate apatinib efficacy.

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