ONCO-1: Development of a univariate tool based on tumor volume to predict 1-year mortality in patients with oral cavity squamous cell carcinoma

ABSTRACT

Objective:  To develop a simple and accessible univariate tool based on tumor volume to predict 1-year mortality in patients with oral cavity squamous cell carcinoma.

Methods:  A retrospective analysis was conducted on patients with oral cavity squamous cell carcinoma included in the GENCAPO cohort. Tumor volume was calculated using the formula of Monga et al. Classification and regression trees were used to define risk categories after determining the volume cutoff points. Logistic regression using 1-year vital status (alive or deceased) as the outcome was employed to construct the Oral Neoplasm Clinical Outcome in 1 year (ONCO)-1 score. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve and the Hosmer-Lemeshow test, respectively. After building the model, we compared it with the TNM system (8th edition) using Kaplan-Meier curves.

Results:  ONCO-1 showed good stratification ability, with a progressive increase in odds ratios for 1-year mortality: odds ratio 2.47 (G2), 4.01 (G3), and 9.99 (G4), compared to group G1. The model demonstrated good discrimination (area under the curve=0.7031) and excellent calibration (Hosmer-Lemeshow p=0.99978). Kaplan-Meier curves indicated a superior prognostic performance of ONCO-1 compared to TNM classification in predicting 1-year survival.

Conclusion:  Tumor volume proved to be a robust predictor of short-term mortality in oral cavity squamous cell carcinoma. ONCO-1 provided better 1-year risk stratification compared to TNM and may contribute to more individualized clinical decision-making.

Keywords:
Mouth neoplasms; Carcinoma, squamous cell; Tumor burden; Prognosis; Mortality; Logistic models; Decision trees

Highlights

We developed ONCO-1, a simple tool based on tumor volume.

Cutoff points were defined by a CART model.

Tumor volume was a prognostic factor for 1-year mortality.

Tumor volume can to support clinical decision-making.

In Brief

A univariate logistic model based on tumor volume, ONCO-1, was capable of predicting the 1-year survival of patients with oral cavity squamous cell carcinoma. Its prognostic performance was superior to the current TNM model, demonstrating better predictive ability compared to a 12-month adaptation of TNM. This suggests that tumor volume should be considered as a prognostic factor and may be useful in clinical decision-making.

INTRODUCTION

Head and neck cancer is currently the sixth most common type of cancer worldwide.(¹) According to GLOBOCAN 2022, when combining data from cancers of the oral cavity, larynx, oropharynx, and hypopharynx, 771,694 new cases were reported.(²) In Brazil, the annual estimate for the 2023-2025 period from the National Cancer Institute (INCA - Instituto Nacional de Câncer) was 15,100 new cases of oral cavity carcinoma (OCC), corresponding to an estimated risk of 6.99 cases per 100,000 inhabitants.(³) Regarding histological type, 90% of diagnoses are squamous cell carcinoma (SCC).(⁴)

Greater accuracy in the clinical staging of oral cavity squamous cell carcinoma (OCSCC) is a priority to optimize prognostic evaluation and ensure that patients receive appropriate care. For OCSCC, the staging system used is the TNM classification of the American Joint Committee on Cancer, 8th edition. This system considers the anatomical aspects of the primary tumor (T), regional lymph node metastases (N), and distant metastases (M), subsequently classifying the patient into four main stages.(⁵) However, because the classification is based on risk categories, the stage assigned to a given patient does not always correspond to an accurate estimate of an individual's risk of death. Thus, one of the main criticisms of the TNM system is the grouping of heterogeneous samples of patients, in which individuals with distinct prognoses are allocated to the same group.(⁶)

Accurate short-term prognostic evaluation allows for better access to and mobilization of resources, and contributes to the development of health policy changes.(⁷) However, the TNM system only estimates 5-year survival, offering no data on shorter-term outcomes. It is important to consider that patients and families should actively participate in therapeutic decisions once knowledge of short-term prognosis enables a clearer understanding of disease progression, allowing for more informed choices that are compatible with patient's personal values and care priorities.(⁸)

In this context, tumor volume has been investigated as an important predictive factor for appropriate staging of OCSCC, showing significant value in treatment outcome evaluation, and serving as an independent factor for disease-free and overall survival.(⁹,¹⁰) Some studies suggest that tumor volume calculated through imaging methods may be a better predictor of overall survival than the TNM system.(¹¹,¹²)

The choice of imaging method for proper evaluation of OCSCC should consider not only diagnostic precision, but also economic feasibility and method availability. Contrast-enhanced computed tomography (CT) is capable of assessing tumor invasion, bone involvement, and cervical lymph node metastases.(¹³) Magnetic resonance imaging (MRI) has superior soft tissue assessment capacity and may be more effective in detecting perineural invasion; however, it is less accessible.(¹⁴) Positron emission tomography (PET/CT), despite its high sensitivity for detecting cervical nodal metastases, presents difficulties in terms of availability and financial viability. Thus, contrast-enhanced CT has become the most practical and accessible choice for evaluating OCSCC.(¹⁵)

Accurate clinical staging of OCSCC is essential to ensure a more precise prognostic evaluation and, consequently, better patient care. In this context, a key question arises: is it possible to develop a simple, univariate tool based on tumor volume capable of predicting 1-year mortality in patients with OCSCC?

OBJECTIVE

To develop ONCO-1 (Oral Neoplasm Clinical Outcome in 1 year), a predictive model based solely on tumor volume.

METHODS

This was a retrospective cohort study including patients prospectively enrolled in the database of the Head and Neck Cancer Genome Project (GENCAPO - Genoma do Câncer de Cabeça e Pescoço) between July 4, 2000, and August 16, 2011, diagnosed with OCSCC. We analyzed the data of patients diagnosed with OCSCC in four hospital centers in the state of São Paulo, Brazil.

The data used are part of a subproject of the study "Environmental, clinical, histopathological and molecular factors associated with the development and prognosis of head and neck squamous carcinomas", conducted in the Department of Surgery of the Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (FMUSP), and previously approved by the Ethics Committee for Analysis of Research Projects (CAPPesq - Comissão de Ética para Análise de Projetos de Pesquisa) under No. 0511/07. The study was conducted in accordance with the ethical standards of the institutional and national research committee. All participants provided written informed consent at the time of inclusion.

Data on the three tumor dimensions (length, width, and depth) were obtained at the time of diagnosis. After data collection, some patients were identified as having missing information regarding tumor dimensions. To avoid excluding these patients from the study, data imputation was performed using shrinkage and regression techniques, preserving the variability of the dataset. Initially, a linear regression model was applied using pathological anatomy measurements from patients with available data. When this was not feasible, the mean of available measurements in each clinical staging category and tumor site was used. In cases where none of the methods were able to impute the clinical dimensions, we applied a shrinkage covariance estimator. Tumor volume was subsequently analyzed using the formula described by Monga et al.(¹⁶) [(length (DC1) × width (DC2) × depth (thickness) /2)]. The tumor volume measurement and its predictive value for 1-year patient outcomes were considered.

A Classification and Regression Trees (CART) model was used to categorize tumor volume, defining three cutoff points and four risk categories. A logistic regression model was then constructed using the four groups to create a predictive tool. To evaluate the accuracy of this model, the area under the receiver operating characteristic curve (AUC) was used for discrimination,(¹⁷) and the Hosmer-Lemeshow test was applied for calibration.(¹⁸) Finally, the outcomes predictive capacity of ONCO-1 was compared to that of the TNM 8th edition using Kaplan-Meier survival curves.(¹⁹)

All statistical analyses were performed using JMP Pro 10.0.0 (SAS Institute Inc., Cary, NC, USA).

RESULTS

The study involved 752 patients diagnosed with OCSCC between July 4, 2000, and August 16, 2011. The patients were mostly male, in the fifth and sixth decades of life, with a history of smoking and alcohol use, as described in table 1.

Table 1
Demographic data of the patients with oral cavity squamous cell carcinoma included in the study (n=752)

Variable		n (%)
Sex
	Female	144 (19.10)
	Male	607 (80.80)
	Unknown	1
Age (years)
Mean [range]		57 [21-93]
Smoking history		660 (88.80)
Alcohol use		615 (83.00)

The mean tumor volume of the patients was 23.45cm³±1.66cm³. Using a CART model, the initial cutoff point identified was 18.62cm³, and after secondary divisions, three final cutoff points were established: 3.4cm³, 18.62cm³, and 63.43cm³, resulting in four risk categories based on mortality (Figure 1).

Figure 1
Classification and Regression Trees (CART) model based on tumor volume to define risk categories for 1-year mortality

The groups were designated as: G1 ≤3.0cm³; 3.0cm³<G2 ≤18.0cm³; 18.0cm³<G3 ≤60cm³; and G4 >60cm³ (Table 2). Following logistic regression analysis, odds ratios were determined (Table 3). Once the cutoff points were defined, the area under the curve (AUC) was used to test the model's discrimination ability. An AUC value of 0.7031 was determined (Figure 2).

Table 2
Distribution of patients across the tumor volume-based risk categories

Tumor volume
Mean	23.45cm3
G1 (≤ 3.0 cm3), n (%)	199 (26.40)
G2 (3.0-18.0 cm3), n (%)	306 (40.70)
G3 (18.0-60 cm3), n (%)	189 (25.10)
G4 (> 60 cm3), n (%)	57 (7.50)

• Distribution of patients across tumor volume-based risk categories derived from Classification and Regression Trees (CART) analysis. Cutoff points: G1 ≤3.0cm³, G2=3.0-18.0cm³, G3=18.0-60.0cm³, and G4 >60.0 cm³.

Table 3
Logistic regression analysis

Risk category	Odds ratio	95% CI	pvalue
G1	-	-
G2	2.47	1.29-4.96	0.0054
G3	4.01	2.02-8.34	<0.0001
G4	9.99	3.72-28.4	<0.0001

• Results of univariate logistic regression model predicting 1-year mortality according to ONCO-1 tumor volume risk groups. G1 was used as a reference. Odds ratios (OR) with 95% confidence intervals (CI) and p-values are shown.

Figure 2
Receiver operating characteristic curve of the ONCO-1 logistic regression model

The predictive performance of the model in terms of calibration was assessed using the Hosmer-Lemeshow test, with a p-value of 0.99978 (Table 4). Kaplan-Meier survival curves comparing ONCO-1 and TNM revealed that ONCO-1 had greater accuracy in outcome prediction for 1-year survival outcomes compared to TNM (Figure 3).

Table 4
Hosmer-Lemeshow test for calibration of the logistic regression model

Calibration	Risk category	Mean risk	Observed mortality
0-25%	G1	0.15	0.13
25.1-50%	G2	0.26	0.23
50.1-75%	G3	0.41	0.40
75.1-100%	G4	0.58	0.54
p value			0.99978

• Calibration of the logistic regression model by ONCO-1 risk category using the Hosmer-Lemeshow test. Comparison between mean predicted risk and observed 1-year mortality. Excellent model calibration was indicated by a p-value of 0.99978.

Figure 3
Kaplan-Meier survival curves comparing ONCO-1 (A) and TNM (8th edition) (B) systems in predicting 1-year survival in OCSCC compared to the TNM

DISCUSSION

In this study we demonstrate that the univariate logistic model based on tumor volume, ONCO-1, was capable of predicting the 1-year survival of patients with OCSCC. ONCO-1 was superior to the currently used TNM model, demonstrating better predictive ability compared to a 12-month adaptation of TNM.

Treatment of OCSCC varies according to stage and severity, with final decisions made by a multidisciplinary team considering tumor control, functionality, and patient quality of life. Surgery is the treatment of choice for all resectable cases, including locally advanced tumors. In early-stage cases, surgery alone is usually sufficient, with no need for additional therapies. In locally advanced disease, the approach becomes more complex and treatment is generally multimodal, with the possibility of combining radiotherapy or chemotherapy to improve local control and survival.(²⁰,²¹) In cases of very advanced or unresectable disease, chemoradiotherapy may be used as an attempt at locoregional control, although this strategy has limited success rates and high toxicity.(²²)

Thus, OCSCC treatment presents points of discussion, particularly concerning the balance between disease control and functionality preservation.(²³,²⁴) One example is the extent and necessity of adjuvant treatment in patients with intermediate-risk features, who may not benefit from aggressive additional therapies.(²⁵) Another issue regarding treatment adequacy is the TNM grouping system into broad risk categories, where individuals with different profiles may share the same stage, especially in locally advanced cases.(²⁶) This hinders therapeutic decisions because these patients may follow different prognoses depending on their individual characteristics.(²⁷,²⁸) The need for precise prognostic information and therapeutic planning encourages the search for tools that enhance staging accuracy.

Previous studies have raised the possibility of using tumor volume as a tool for prognostic assessment. While some authors, such as Tofanelli et al., suggest that tumor volume may be used as a complement to the TNM system,(²⁹) others indicate that it functions as an independent risk factor for prognostic evaluation, showing greater accuracy in predicting the risk of nodal metastases and extracapsular spread than the TNM in OCSCC staging.(³⁰)

Use of tumor volume as a prognostic marker has also been studied in other cancer sites. In gastric cancer, it outperformed tumor diameter and T-stage, improving prognostic precision.(³¹) In non-small cell lung cancer, tumor volume was a better predictor of overall survival than tumor diameter.(³²) In colorectal cancer, CT-based volume assessment correlated significantly with disease stage and patient survival.(³³)

Staging accuracy in OCSCC influences both prognosis and treatment planning. Detailed assessment of tumor extension is crucial for surgical planning,(³⁴,³⁵) and accurate staging avoids unnecessary treatments and associated morbidity.(³⁶) CT-based volume measurement supports practical and reproducible application, especially in low-resource settings where MRI and PET/CT are less accessible.(³⁷) CT also offers good anatomical definition compared to MRI and PET/CT. In oral cavity tumors, the puffed-cheek technique–asking patients to inflate their cheeks during imaging–enhances delineation, especially for tumors along the gingiva and buccal mucosa.(³⁸)

In this study, a decision tree model (CART) was applied a retrospective cohort of patients diagnosed with OCSCC to define three cutoff points for tumor volume, resulting in four risk categories for 1-year mortality. Patients were allocated according to tumor volume, and when comparing the odds ratios among the groups, a clear trend of progressively increased mortality risk was observed as tumor volume increased. Patients classified in the highest risk group (G4) had a tenfold greater probability of death within 1 year compared to those in the lowest risk group (G1), reinforcing the prognostic relevance of volume as a continuous variable. Likewise, intermediate groups (G3 and G2), when compared to G1, showed fourfold and twofold higher chances, respectively, of adverse outcomes within the evaluated period. These findings highlight the ability of the ONCO-1 to adequately stratify patients based on a single, objective, and accessible parameter, reflecting a consistent risk gradient. Moreover, they suggest that tumor volume is a valuable marker to guide individualized clinical decisions by capturing nuances in disease extent that the TNM system may not discriminate well.

Kaplan-Meier curves showed that ONCO-1 more accurately predicted 1-year outcomes than the 12-month adapted TNM system. Survival analysis demonstrated that patients classified by ONCO-1 had significantly different survival rates over the 12-month period, showing superior risk discrimination. This finding enables ONCO-1 to overcome the limitations of TNM in providing short-term predictions.

This study offers valuable clinical contributions due to the high quality of prospectively collected data by trained interviewers and the practicality of the tool developed. ONCO-1 uses tumor volume, a simple, accessible, and replicable measure. This allows more objective and standardized prognostic assessment, providing physicians and researchers with a reliable resource in the prognostic evaluation of OCSCC.

However, the study has a few limitations. We are unaware of a specific 1-year prognostic model that could serve as a direct reference, which makes accurate comparison difficult. Furthermore, the TNM system, used here in a 1-year simulation, was originally designed to evaluate 5-year outcomes, which may have influenced the comparison results with ONCO-1.

CONCLUSION

Regarding the central question of whether tumor volume can independently predict 1-year mortality in patients with oral cavity squamous cell carcinoma, we found that tumor volume is indeed a robust prognostic factor. Based on this variable as a foundation and employing machine learning techniques combined with logistic regression, it was possible to develop a univariate logistic model. ONCO-1 showed good discrimination and calibration for predicting 1-year survival, based on easily retrievable information, which outperformed the TNM system. These findings suggest that tumor volume should be considered a prognostic factor and may be useful in clinical decision-making. External validation of these findings is required to ensure that ONCO-1 can be consistently applied in different settings. Moreover, these findings should be compared to other prognostic models specifically designed for 12-month outcomes, aiming to provide a reliable and accessible tool to support effective and individualized patient risk stratification and care.

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Edited by

• Associate Editor:
  Claudio Roberto Cernea Hospital Israelita Albert Einstein, São Paulo, SP, Brazil ORCID: https://orcid.org/0000-0001-5899-0535

Publication Dates

• Publication in this collection
  05 Dec 2025
• Date of issue
  2025

History

• Received
  18 May 2025
• Accepted
  16 July 2025