Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis

doi:10.21203/rs.3.rs-8006282/v1

Objective

To evaluate quantitative imaging biomarkers, including MRI tumor size, apparent diffusion coefficient (ADC), arterial-phase enhancement, and PET maximum standardized uptake value (SUVmax), as prognostic indicators of overall survival (OS), recurrence-free survival (RFS), and treatment response in cervical squamous cell carcinoma (SCC).

Materials and Methods

Fifty patients with biopsy-proven SCC who underwent pre- and post-treatment MRI and FDG-PET/CT were retrospectively analyzed. Tumor dimensions (axial, sagittal), ADC, SUVmax, and arterial enhancement were assessed. Survival was estimated by Kaplan–Meier, and associations with OS and RFS were tested using Cox regression. Logistic regression was used to determine predictors of treatment response according to RECIST and PERCIST criteria.

Results

Tumor size, SUVmax, and ADC changed significantly post-treatment (p < 0.001). Persisting arterial enhancement was seen in 35% of tumors. Higher post-treatment SUVmax predicted worse OS (HR = 1.078, p = 0.008) and RFS (HR = 1.049, p = 0.046). Larger residual sagittal tumor size showed borderline associations with inferior OS (HR = 1.38, p = 0.089) and RFS (HR = 1.31, p = 0.057). Increases in tumor size significantly correlated with persistent arterial enhancement (axial OR = 1.58, p = 0.025; sagittal OR = 1.93, p = 0.002). Persistent enhancement trended toward worse RFS (HR ≈ 2.24, p = 0.107). In multivariable analysis, post-treatment pelvic lymph node positivity remained independently associated with poorer RFS. Baseline ADC predicted metabolic response: a higher pre-treatment ADC was associated with a failure to achieve a partial metabolic response per PERCIST (OR = 1.007, p = 0.015).

Conclusion

Post-treatment SUVmax, residual tumor size, arterial enhancement, and pelvic lymph node involvement are key prognostic indicators in cervical SCC. Persistent enhancement may reflect treatment-resistant vascularity. Integrating these biomarkers into clinical workflows may improve risk stratification and guide personalized management.

SUVmax

size

enhancement

cervix. ADC

Cervical cancer remains a major global health challenge, representing the fourth most common cancer among women worldwide [1]. Despite advances in screening and vaccination programs, a substantial number of patients continue to present with locally advanced disease requiring definitive chemoradiotherapy. Prognosis is typically assessed using clinical staging systems such as FIGO; however, these rely on morphologic findings and provide limited insight into tumor biology and treatment response [2]. Consequently, there is increasing interest in identifying noninvasive imaging biomarkers that can refine prognostication, guide therapeutic decision-making, and detect early signs of treatment resistance.

Magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are routinely employed in the evaluation of cervical cancer [2]. MRI offers excellent soft-tissue resolution, enabling the assessment of tumor dimensions, local invasion, and functional parameters, such as the apparent diffusion coefficient (ADC), through diffusion-weighted imaging [3]. PET/CT provides complementary metabolic information by measuring the maximum standardized uptake value (SUVmax), which reflects tumor glycolytic activity [4]. Dynamic contrast-enhanced MRI further enables the evaluation of tumor vascularity, with arterial-phase enhancement serving as a potential indicator of persistent angiogenesis [5] (Fig. 1). Collectively, these modalities capture different aspects of tumor phenotype, yet their integrated role in predicting outcomes in cervical squamous cell carcinoma (SCC) remains underexplored.

Previous studies have suggested associations between post-treatment imaging features and survival; however, small sample sizes, heterogeneous histologies, or incomplete follow-up have limited the findings of most. In particular, the prognostic value of arterial enhancement and lymph node involvement has not been systematically examined with quantitative biomarkers such as ADC and SUVmax. Moreover, the predictive utility of these markers in defining radiologic (RECIST) and metabolic (PERCIST) treatment responses has not been fully characterized. This study investigated the prognostic significance of quantitative imaging biomarkers, including tumor size, ADC, SUVmax, and arterial enhancement, on overall survival (OS), recurrence-free survival (RFS), and treatment response in patients with cervical SCC.

Study Design and Population

This retrospective cohort study included patients with pathologically confirmed squamous cell carcinoma (SCC) of the cervix who underwent both pelvic MRI and 18F-FDG PET/CT before and after definitive treatment between January 2010 and December 2020 at our institution. Institutional review board approval was obtained, and informed consent was waived due to the retrospective design.

Patients were included if they had a histologically confirmed diagnosis of cervical squamous cell carcinoma (SCC) and had completed both baseline pelvic MRI and 18F-FDG PET/CT within six weeks before starting chemoradiotherapy. Post-treatment MRI and PET/CT were required within twelve weeks of completing therapy, along with at least twelve months of clinical follow-up or documentation of recurrence or death within that period. Only studies with adequate image quality, allowing for quantitative analysis of tumor size, apparent diffusion coefficient (ADC), SUVmax, and arterial enhancement, were included. Patients were excluded if they had non-squamous histology (such as adenocarcinoma, adenosquamous, or small-cell types), prior hysterectomy, pelvic radiation, or systemic therapy before baseline imaging. Exclusion also applied to cases lacking either pre- or post-treatment MRI or PET/CT, incomplete or poor-quality imaging data that prevented quantitative assessment, evidence of distant metastasis beyond the pelvic or para-aortic nodes that precluded curative-intent treatment, or insufficient follow-up of less than twelve months without documentation of recurrence or death. Of the 1,000 patients identified in Montage, only 50 patients were included in the study, as they met the criteria.

Imaging Analysis

All patients underwent pre- and post-treatment pelvic MRI and 18F-FDG PET/CT on clinically approved scanners under standardized protocols. For PET/CT, patients fasted for at least 6 hours before 18F-FDG administration, and serum glucose was confirmed to be < 120 mg/dL before tracer injection. An intravenous dose of 185–370 MBq (5–10 mCi) of 18F-FDG was administered, followed by a 60-minute uptake period in a quiet room. Patients were instructed to void their bladder immediately before imaging to minimize pelvic urinary artifact. PET/CT was performed on integrated GE Discovery systems (GE Healthcare). Non-contrast CT was acquired helically from the skull base to the mid-thigh using 120 kVp, 300 mAs, 3.75-mm slice thickness, and a 0.5-second rotation. PET data were reconstructed using ordered subset expectation maximization (OSEM) algorithms, yielding attenuation-and non-attenuation-corrected images that were reviewed in axial, sagittal, and coronal planes, with fused PET/CT images for interpretation.

Pelvic MRI was performed on a 1.5 T or 3.0 T system (GE Healthcare) using a phased-array torso coil. Sequences included axial and sagittal T2-weighted fast spin-echo (FSE), axial T1-weighted pre- and post-contrast sequences, diffusion-weighted imaging (DWI; b values 0, 500, and 1000 s/mm²) with corresponding ADC maps, and dynamic contrast-enhanced (DCE) MRI using gadobutrol (0.1 mmol/kg). Arterial-phase enhancement was assessed on early post-contrast sequences. Tumor size was measured as the maximum diameter in axial and sagittal planes on T2-weighted MRI. ADC values were obtained by manually placing regions of interest (ROIs) within the solid portion of the tumor, avoiding necrotic or hemorrhagic areas. PET/CT SUVmax was measured using 3D spherical volumes of interest over the most metabolically active tumor region.

Pelvic lymph nodes were considered positive on MRI if they demonstrated a short-axis diameter ≥ 1.0 cm, central necrosis, or irregular margins, and on PET/CT if they exhibited focal FDG uptake above background activity. Imaging analyses were performed by two radiologists (with 15- and 24-year experience in oncologic imaging) specializing in gynecologic imaging, with consensus used to resolve discrepancies.

Statistical Analysis

All statistical analyses were conducted using R software (version 4.3.1, R Development Core Team). Continuous variables, including tumor size, ADC, and SUVmax, were summarized with means, standard deviations, medians, and ranges. In contrast, categorical variables, such as arterial enhancement and lymph node status, were summarized with frequencies and percentages. Paired comparisons of pre- and post-treatment imaging biomarkers were performed using the Wilcoxon signed-rank test for continuous measures and the McNemar test for categorical features.

Overall survival (OS) and recurrence-free survival (RFS) were estimated using the Kaplan–Meier method, with survival probabilities reported at 2, 5, and 10 years. Associations between imaging biomarkers and survival outcomes were assessed using univariate Cox proportional hazards models; hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Variables with p < 0.10 in univariate analysis were considered for multivariable Cox regression. Logistic regression was used to evaluate predictors of treatment response, as defined by the RECIST 1.1 and PERCIST criteria.

Patient Characteristics

Fifty patients with histologically confirmed squamous cell carcinoma (SCC) of the cervix met the inclusion criteria and were included in the final analysis. The mean age was 46.4 years (SD, 14.1; range, 23–79 years). Median clinical follow-up was 46.5 months.

Treatment Outcomes and Survival

At the last follow-up, 10 patients (20%) had died, and 16 (35%) had experienced recurrence or death (Table 1). Median overall survival (OS) was 13.5 years (95% CI: 9.4). Estimated OS rates were 91.4% at 2 years, 82.7% at 5 years, 77.5% at 8 years, and 64.6% at 10 years. Recurrence-free survival (RFS) rates were 72.6% at 2 years and 57.7% at 5 years (Table 2).

Table 1

Overall Survival (OS) – Kaplan-Meier analysis.
Statistic	Value
N	50
Events (Deaths)	10 (20%)
Median Survival	13.49 years (95% CI: 9.38 – NA)
2-year Survival	91.4% (95% CI: 83.7–99.8%)
5-year Survival	82.7% (95% CI: 71.5–95.5%)
8-year Survival	77.5% (95% CI: 64–93.9%)
10-year Survival	64.6% (95% CI: 43–96.9%)

This table presents overall survival data for 50 patients who were followed from the time of diagnosis. Median survival was 13.49 years with 10 events (20%). Survival probabilities at 2, 5, 8, and 10 years are reported with 95% confidence intervals (CI).

Table 2

Recurrence-Free Survival (RFS) – Kaplan-Meier analysis.
Statistic	Value
N	46
Events (Recurrence or Death)	16 (35%)
Median Survival	Not reached (95% CI: 3.66 – NA)
2-year RFS	72.6% (95% CI: 60.4–87.1%)
5-year RFS	57.7% (95% CI: 42.9–77.5%)

This table presents recurrence-free survival data for 46 patients after treatment, with 16 recurrence or death events (35%). Median RFS was not reached. Two- and five-year RFS probabilities with 95% CI are reported.

Imaging Biomarkers: Pre- vs. Post-Treatment

All imaging biomarkers showed significant post-treatment changes (Table 3). Tumor size: Median axial diameter decreased from 4.7 cm to 1.2 cm (p < 0.001); sagittal diameter decreased from 3.6 cm to 1.1 cm (p < 0.001). ADC values: Median ADC increased from 0.82 ×10^–3 mm²/s to 1.35 ×10^–3 mm²/s (p < 0.001). SUVmax: Median SUVmax decreased from 17.8 to 4.8 (p < 0.001). Arterial enhancement: Present in 98% of tumors pre-treatment and persisted in 35% post-treatment. Pelvic lymph nodes: Involvement decreased from 72% on MRI and 52% on PET at baseline to 20% and 8% after treatment.

Table 3

Tumor Size, ADC, and SUV Changes (Wilcoxon test).
Measure	Pre-treatment (Mean ± SD)	Post-treatment (Mean ± SD)	Difference (Mean ± SD)	Median (Range) Change	p-value
Tumor Size Axial (cm)	4.5 ± 1.7	1.6 ± 1.7	-2.9 ± 1.9	-2.8 (-7.8, 1.6)	< 0.001
Tumor Size Sagittal (cm)	3.8 ± 1.6	1.4 ± 1.6	-2.4 ± 2.1	-2.3 (-6.3, 2.0)	< 0.001
ADC (×10⁻³ mm²/s)	847.6 ± 151.1	1367.6 ± 374.3	+ 512.4 ± 409.4	510 (-316, 1736)	< 0.001
SUV Max	19.4 ± 9.0	7.1 ± 6.5	-12.3 ± 9.6	-10.9 (-40.9, 10.4)	< 0.001

This table summarizes pre- and post-treatment measurements for tumor size (axial and sagittal), apparent diffusion coefficient (ADC), and maximum standardized uptake value (SUVmax). Mean, standard deviation, and median with range are reported. Differences were assessed using the Wilcoxon signed-rank test, with statistically significant improvements observed in all parameters (p < 0.001).

Prognostic Associations

On univariate Cox regression, higher post-treatment SUVmax was associated with worse OS (HR = 1.078, 95% CI: 1.02–1.14, p = 0.008) (Table 4) and RFS (HR = 1.049, 95% CI: 1.00–1.10, p = 0.046) (Table 5). Larger post-treatment sagittal tumor size showed borderline associations with inferior OS (HR = 1.38, p = 0.089) and RFS (HR = 1.31, p = 0.057) (Fig. 2).

Table 4

Cox Regression – Predictors of Overall Survival (OS).
Variable	HR (95% CI)	p-value	Conclusion
Age	1.02 (0.98–1.06)	0.422	Not significant
Size Sagittal	1.38 (0.95–1.99)	0.089	Marginal – worse OS
Size Axial	1.22 (0.81–1.82)	0.340	Not significant
ADC	0.63 (0.08–5.17)	0.669	Not significant
SUV	1.04 (0.95–1.13)	0.432	Not significant

This table summarizes the univariate Cox regression analyses correlating age, changes in tumor size (axial and sagittal), changes in ADC, and changes in SUV with overall survival. Increase in sagittal tumor size was marginally associated with worse OS (HR = 1.38, p = 0.089).

Table 5

Cox Regression – Predictors of Recurrence-Free Survival (RFS).
Variable	HR (95% CI)	p-value	Conclusion
Age	1.02 (0.99–1.05)	0.266	Not significant
Size Sagittal	1.30 (0.99–1.71)	0.057	Marginal – worse RFS
Size Axial	1.21 (0.92–1.59)	0.173	Not significant
ADC	0.76 (0.17–3.43)	0.719	Not significant
SUV	1.01 (0.95–1.08)	0.699	Not significant

This table presents univariate Cox regression analyses correlating clinical and imaging variables with RFS. Increased sagittal tumor size was marginally associated with worse RFS (HR = 1.30, p = 0.057).

Logistic regression analysis revealed that a post-treatment increase in axial and sagittal tumor size was correlated with the persistence of arterial enhancement (axial OR = 1.58, p = 0.025; sagittal OR = 1.93, p = 0.002). Persistent arterial enhancement trended toward worse RFS (HR ≈ 2.24, p = 0.107) (Tables 6 and 7). In multivariable analysis, only post-treatment pelvic lymph node positivity on MRI remained independently associated with inferior RFS (p < 0.05).

Table 6

Logistic Regression – Predictors of Post-treatment Arterial Enhancement.
Variable	OR (95% CI)	p-value	Conclusion
Age	1.00 (0.96–1.04)	0.940	Not significant
Size Axial	1.58 (1.10–2.47)	0.025	Significant
Size Sagittal	1.93 (1.33–3.13)	0.002	Significant
ADC	0.21 (0.03–1.10)	0.085	Trend, not significant
SUV	1.04 (0.97–1.11)	0.300	Not significant

This table shows univariate logistic regression analyses of age, changes in tumor size, ADC, and SUV in predicting post-treatment arterial enhancement on imaging. Increases in axial and sagittal tumor size were significantly associated with arterial enhancement (p = 0.025 and p = 0.002, respectively).

Table 7

Post-treatment Predictors of OS and RFS.
Predictor	HR (95% CI)	p-value	Outcome
SUV.Max.post	1.08 (1.02–1.14)	0.008	Worse OS
SUV.Max.post	1.05 (1.00–1.10)	0.046	Worse RFS
ADC Pre/Post	NS	–	Not predictive
Arterial Enhancement Post	HR = 2.24 (0.84–5.97)	0.107	Not significant

This table provides univariate Cox regression results for post-treatment SUV, ADC, and arterial enhancement in relation to OS and RFS. Higher post-treatment SUV was significantly associated with worse OS (p = 0.008) and RFS (p = 0.046).

Predictors of Treatment Response

Baseline ADC values were predictive of metabolic response. Higher pre-treatment ADC was correlated with a failure to achieve a partial metabolic response per PERCIST (OR = 1.007, 95% CI: 1.002–1.014, p = 0.015) (Table 8) (Fig. 3). Baseline tumor size showed a nonsignificant trend toward predicting RECIST response.

Table 8

Predictive of Treatment Response (RECIST and PERCIST).
Predictor	OR (95% CI)	p-value	Conclusion
ADC.pre (PERCIST)	1.007 (1.002–1.014)	0.015	Higher baseline ADC → worse response
SUV.Max.pre	NS	0.347	Not predictive
Tumor Size Sagittal.pre	OR = 0.60 (0.31–1.03)	0.091	Trend
Tumor Size Axial.pre	OR = 0.59 (0.32–0.99)	0.059	Borderline

This table summarizes the results of logistic regression analyses examining the predictive value of pretreatment tumor size, ADC, and SUV for treatment response, using both RECIST and PERCIST criteria. A higher pretreatment ADC was significantly correlated with a poor metabolic response by PERCIST (OR = 1.007, p = 0.015).

SUVmax and PET Metabolic Parameters

Our study demonstrates that post-treatment SUVmax serves as the most significant predictor of survival outcomes in cervical squamous cell carcinoma, with higher values being significantly associated with worse overall survival (HR = 1.078, p = 0.008) and recurrence-free survival (HR = 1.049, p = 0.046). This finding aligns with the substantial body of evidence supporting PET metabolic parameters as robust prognostic indicators in cervical cancer.

A comprehensive meta-analysis of 1313 patients across 14 studies confirmed that patients with high primary tumor SUVmax have significantly shorter overall survival compared to those with low SUVmax (HR 2.582, 95% CI 1.936–3.443, p < 0.001), with similarly adverse effects on event-free survival (HR 1.938, 95% CI 1.203–3.054, p = 0.004) [6]. The superiority of post-treatment over pre-treatment PET parameters has been consistently validated, with an extensive Swedish cohort study of 133 patients demonstrating that post-treatment metabolic parameters exhibit superior prognostic value compared to baseline measurements [7]. A Turkish multi-center study of 194 patients with a 12.5-year follow-up further corroborated these findings, showing that SUVmax independently predicted local recurrence (HR = 1.15, p = 0.001). In contrast, metabolic tumor volume (MTV) and SUVmean were predictive of distant metastasis [8].

Apparent Diffusion Coefficient (ADC)

This study also identifies pre-treatment ADC values as significant predictors of treatment response, with higher baseline ADC values associated with a failure to achieve a partial metabolic response (OR = 1.007, p = 0.015). This finding contributes to the growing evidence supporting ADC as a valuable biomarker for treatment prediction and prognosis in cervical cancer.

A comprehensive meta-analysis of 2,314 patients across 22 studies demonstrated that ADC values achieve excellent diagnostic performance for detecting lymph node metastasis, with a pooled sensitivity of 86% and a specificity of 85%, significantly outperforming conventional imaging methods [9]. The prognostic utility of ADC extends beyond lymph node assessment, with multiple studies establishing its role as an independent predictor of disease outcomes. An extensive Norwegian study of 179 patients revealed that a low tumor ADC mean predicted reduced disease-specific survival (HR = 0.96, p = 0.001), with normalized ADC ratios providing even stronger prognostic information [9]. Another study of 219 patients demonstrated that the mean absolute deviation (MAD) from ADC maps, representing ADC distribution uniformity, served as an independent protective factor against early recurrence (HR = 0.978, p = 0.001) [10]. This suggests tumor heterogeneity assessed through ADC analysis may provide additional prognostic information beyond simple mean values. The superior performance of normalized ADC ratios, particularly myometrium ADC/tumor ADC ratios, highlights the importance of accounting for MRI acquisition-related variations and suggests that relative measurements may be more robust and clinically applicable than absolute ADC values.

The ability of baseline ADC to predict treatment response early in therapy makes it particularly valuable for adaptive treatment strategies, potentially allowing modification of treatment approaches based on predicted response patterns.

Tumor Size Parameters

In addition, our study findings regarding tumor size parameters align with established evidence demonstrating the prognostic significance of baseline tumor dimensions and volumetric changes during treatment. Post-treatment sagittal tumor size showed borderline associations with inferior survival outcomes (HR = 1.38 for overall survival, p = 0.089), reflecting the established principle that larger residual disease portends worse prognosis. A Chinese study of 217 patients provided compelling evidence for the prognostic value of the tumor volume reduction rate (TVRR), identifying it as an independent predictor of prognosis. Patients achieving a TVRR greater than 82.19% demonstrated significantly better outcomes [11]. Mid-treatment tumor volume greater than 11.38 cm³ emerged as an independent predictor of poor survival (HR = 3.192, p = 0.034), emphasizing the importance of assessing early treatment response through volumetric analysis.

The relationship between tumor size parameters and treatment outcomes reflects fundamental principles of tumor biology and the response to treatment. Larger tumors typically harbor greater cellular heterogeneity, increased hypoxic regions, and enhanced resistance mechanisms, reducing treatment sensitivity. The Korean multi-center study demonstrated that a baseline tumor volume greater than 61.6 cm³ was associated with adverse outcomes (HR = 0.417, p = 0.032). In comparison, the threshold of 11.38 cm³ for mid-treatment volume provided optimal discrimination for survival prediction [12]. The prognostic superiority of TVRR over static size measurements suggests that dynamic assessment of treatment response provides more meaningful information than baseline tumor dimensions alone. Patients with poor volume reduction (< 82%) during external beam radiotherapy represent a high-risk population that may benefit from treatment intensification or modified approaches.

Limitations

This study has several limitations. First, its retrospective design introduces the possibility of selection bias and limits control over imaging acquisition timing and treatment heterogeneity. Second, the relatively modest sample size (n = 50) may reduce the statistical power to detect associations, particularly in subgroup analyses such as nodal involvement or arterial enhancement persistence. Third, imaging was performed on multiple MRI and PET/CT platforms over a decade, which may have introduced technical variability in SUV measurements, ADC quantification, and contrast-enhanced sequences. Fourth, inter-observer variability in tumor measurements, ADC placement, and assessment of arterial enhancement was not formally evaluated; thus, the reproducibility of these imaging biomarkers remains to be validated. Fifth, although RECIST and PERCIST criteria were used to define treatment response, histopathologic confirmation of residual disease was not consistently available. Sixth, some patients had limited follow-up, which may have potentially underestimated late recurrences or survival outcomes. Finally, the study focused exclusively on squamous histology, and findings may not generalize to other cervical cancer subtypes such as adenocarcinoma or adenosquamous carcinoma.

Clinical Implications

Given these prognostic associations, patients demonstrating elevated post-treatment SUVmax, larger residual tumor dimensions, or persistent arterial enhancement warrant surveillance protocols. Close monitoring with serial MRI examinations allows assessment of residual tumor size and perfusion characteristics, while PET imaging provides complementary metabolic information to detect early recurrence or metastatic progression. Additionally, regular cervical cytology, including Pap smears, remains essential for identifying local residual disease at the primary site. This integrated multimodality surveillance approach enables early detection of disease progression, potentially facilitating timely therapeutic intervention and improving long-term outcomes in high-risk patients.

Implications for Future Research

The findings from this study highlight the potential of quantitative imaging biomarkers as noninvasive tools for risk stratification in cervical SCC. Future research should focus on validating these prognostic markers in larger, prospective, and multicenter cohorts to improve generalizability and reproducibility. Standardizing imaging acquisition protocols and quantitative measurement techniques, particularly for ADC and SUV, will be critical to reducing inter-institutional variability.

Quantitative imaging biomarkers from MRI and PET provide important prognostic information in cervical squamous cell carcinoma. Higher post-treatment SUVmax, residual tumor size, and persistent arterial enhancement were associated with worse survival outcomes, while pelvic lymph node positivity after therapy emerged as an independent predictor of recurrence. Pre-treatment ADC values correlated with metabolic response, suggesting a role in predicting treatment efficacy.

Author Contribution

Conceptualization, M.V.; P.B. Formal Analysis, S.J. Investigation, A.A.; Data Curation, A.A.; Writing– Original Draft Preparation, M.V., P.B., S.J. Writing– Review & Editing, M.V.; P.B.; S.J.; R.I.; D.G.

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No competing interests reported.

Quantitative Imaging Biomarkers as Prognostic Indicators in Squamous Cell Cervical Carcinoma: A Retrospective Cohort Analysis

Status:

Journal Publication

Version 1

Abstract

Objective

Materials and Methods

Results

Conclusion

Figures

Introduction

Material and Methods

Study Design and Population

Imaging Analysis

Statistical Analysis

Results

Patient Characteristics

Treatment Outcomes and Survival

Imaging Biomarkers: Pre- vs. Post-Treatment

Prognostic Associations

Predictors of Treatment Response

Discussion

SUVmax and PET Metabolic Parameters

Apparent Diffusion Coefficient (ADC)

Tumor Size Parameters

Limitations

Clinical Implications

Implications for Future Research

Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Journal Publication

Version 1