Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Abstract

Background

Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment.

Purpose

To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer.

Material and Methods

X-tile was used to calculate the optimal threshold for ΔADC_mean(%) for prognostic stratification. Kaplan–Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model.

Results

ΔADC_mean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADC_mean(%) to diagnose prognosis was 40.8. Kaplan–Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years (P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862.

Conclusion

ΔADC_mean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADC_mean(%) predict PFS of patients with cervical cancer with moderate accuracy.

Keywords

Cervical cancer concurrent chemoradiotherapy tumor downstaging prognosis nomogram

Introduction

Cervical cancer is the most common malignancy in the female reproductive system. Due to the lack of specific clinical symptoms in the early stages, some patients are already at the locally advanced or advanced stage when they are first diagnosed. The International Federation of Gynecology and Obstetrics (FIGO) 2018 Cancer Report: Guidelines for New Staging and Diagnosis and Treatment of Cervical Cancer states that concurrent chemoradiotherapy (CCRT), which includes external beam radiation therapy (EBRT), platinum-based chemotherapy (PBCT), and intracavitary brachytherapy (ICR), is the preferred treatment for locally advanced (IB3∼IIA2) and mid-advanced (IIB∼IVA) cervical cancer (1). Reducing tumor stage by CCRT helps to alleviate patients’ clinical symptoms (2), but the risk of recurrence and metastasis in locally advanced cervical cancer is still more than 20% due to initial invasive tumor growth (3), with a median survival of only 16.8 months (4,5). Therefore, monitoring the early efficacy of CCRT for cervical tumors is of vital importance in guiding clinical control of the dosage, duration of chemoradiotherapy, and timely adjustment of treatment plans to improve patient outcomes and long-term prognosis.

CCRT resistance and residual lesions are high-risk factors for recurrence and distant metastasis in patients with cervical cancer, which was markedly associated with efficacy and short-term prognosis. Currently, tumor volume change analysis is widely used to evaluate CCRT efficacy, but this method only reflects changes in tumor dimensions and cannot show changes in the internal metabolism and invasiveness to surrounding tissues of the tumor. Magnetic resonance imaging (MRI) provides high soft tissue resolution, enabling the visualization of tumor morphology and the extent of tumor infiltration into the surrounding soft tissues, making it the preferred imaging modality for cervical cancer (6,7). The apparent diffusion coefficient (ADC) was significantly related to cell density and tissue water molecule mobility. It can provide effective information for clinical assessment of tumor malignancy, invasiveness, and treatment efficacy, and is a potential imaging biomarker for most tumor treatment outcomes and prognosis (8–11). ΔADC_mean(%) is based on the changes within the tumor to reflect its sensitivity to treatment. Research employing the percentage change in ADC (ΔADC_mean(%)) to predict the therapeutic efficacy of CCRT in cervical cancer remains scarce and is predominantly characterized by limited sample sizes and short-term studies. Furthermore, these investigations often lack rigorous internal validation. In addition, there are no existing reports on the use of ΔADC_mean(%) to predict tumor regression in cervical cancer after CCRT. Therefore, the aim of our study was to investigate the clinical value of ΔADC_mean and clinical prognostic factors (CPF) to predict tumor downstaging after CCRT and 3-year postoperative progression-free survival (PFS) in locally advanced cervical cancer, and to produce a nomogram to construct a visual prognostic model.

Material and Methods

Patients

The present study was approved by the ethics committee (reference no. 2023YY095). Due to the retrospective nature of this study, the requirement for informed consent from participants was waived. Clinical and imaging data were collected from 148 patients with cervical cancer diagnosed by pathological biopsy and treated with CCRT between February 2017 and February 2022. Among them, 27 cases were excluded due to incomplete or poor-quality imaging (n = 20) and artifacts from intrauterine devices or hip replacement surgery (n = 7). In total, 121 patients who met the inclusion criteria were included in this study.

Image analysis

MRI scans were acquired before the start of CCRT and approximately 1 month after the end of treatment. All images were analyzed in consensus by two radiologists (A and B, with 10 years and 3 years of experience in MRI of the abdominopelvic region, respectively) on our PACS system to observe and record the tumor staging before and after CCRT by selecting the coronal, sagittal, and organ-axis T2-weighted (T2W) imaging non-compacted lipid sequences, respectively. They selected the largest cross-section of the tumor on the ADC map phase, measured the mean ADC value at that level avoiding the cystic necrotic area, then calculated the rate of change between the two means. The maximum tumor diameter, pelvic lymph node metastasis, and involvement of the surrounding pelvic soft tissues were also measured. Consistency analysis was performed on the measurements of the two radiologists. The formula for calculating the pre-treatment ADC_mean and post-treatment ADC_mean change rate (ΔADC_mean(%)) was as follows: (Post-treatment ADC_mean–Pre-treatment ADC_mean) / Pre-treatment ADC_mean× 100 (%).

MRI method

All MRI scans were performed using a 1.5-T MRI device (Avanto; Siemens, Germany) equipped with a phased-array body coil, and the scanning range extended from the superior border of the ilium to the lower border of the pubic symphysis. The scan sequences and parameters included routine T1-weighted (T1W) imaging in the body axis, fat-suppressed T2W imaging in the body axis, non-fat-suppressed T2W imaging in sagittal, coronal, and organ-axis orientations, and diffusion-weighted imaging (DWI) in the body axis. Gradient echo sequences were harnessed at b-values of 0–1000. The sequence parameters were set with a repetition time (TR) of 4000 ms and an echo time (TE) of 87.0 ms. The slice thickness was defined at 3.0 mm with an inter-slice gap of 2.0 mm, and a field of view (FOV) meticulously calibrated at 240 × 240 mm.

CCRT protocol

All patients received extracorporeal radiation therapy along with chemotherapy. External irradiation was prescribed at a total dose of 45 Gy, 1.3–2 Gy per session, five times per week. Concurrent platinum-containing chemotherapy was administered during radiotherapy at 30–40 mg/m² per session, once a week, for a total of 4–6 sessions.

Clinical and histopathologic factors

Approximately 1 month after completing CCRT (detailed treatment protocols can be found in the supplementary material), all patients underwent pelvic MRI scans for clinical restaging and treatment response assessment. Follow-up pelvic MRI and abdominal CT examinations were performed every 3–4 months during the first 2 years, and every 6 months for the next 3 years, with physical examination and tumor marker serology performed concurrently at each follow-up visit to monitor for tumor recurrence. Suspicious lesions were subjected to pathological examination to confirm recurrence and record the time of recurrence.

Clinical outcomes

Successful downstaging was defined as a reduction in viable tumor burden after CCRT to within RECIST. PFS was defined as the time from the start of treatment until the first suspicion of recurrence or metastasis on imaging confirmed by pathological examination.

Statistical analysis

Prism was used to analyze the relationship between downstaging and prognosis. Receiver operating characteristic (ROC) curves were used to calculated the area under the ROC curve (AUC), sensitivity, and specificity of baseline ADC parameters for predicting tumor downstaging, and X-tile was used to calculate the optimal threshold for ΔADC_mean(%) to predict tumor downstaging.

The population eligible for follow-up time was selected and randomized into training and validation groups in a 2:1 ratio. Imaging parameters were analyzed using LASSO Cox regression to calculate the corresponding risk index. The optimal threshold for the risk index was assessed using X-tile to derive the corresponding ΔADC% value. The Kaplan–Meier analysis was used to assess the difference in 1–3-year PFS within each group under the threshold. Univariate Cox regression analysis was performed to screen for high-risk factors for imaging parameters and clinical information associated with PFS (P < 0.01) and multivariate analysis was performed to screen for independent risk factors associated with PFS (P < 0.05) to produce a nomogram for prognosis. ROC curves and decision curves were plotted for the training group and the AUC and consistency index (C-index) were calculated. Validation groups are used for validation.

Results

Baseline characteristics

ΔADC_mean(%) value predicts tumor downstaging value

Among 121 patients, 93 showed tumor downstaging 1 month after completing CCRT. An example of downstaging is shown in Fig. 1. The patient’s imaging parameters are shown in Table 1. Kaplan–Meier curves showed that PFS was significantly higher in patients with declining tumor re-staging than in those without (P < 0.004) (Fig. 2). ROC curves calculated that ΔADC, ΔADC_mean(%), pre-treatment ADC value, and post-treatment ADC value could be used to predict tumor downstaging, and ΔADC_mean(%) had the highest predictive value, with an AUC value of 0.868 (95% confidence interval [CI] = 80.0–91.9), sensitivity of 83.33%, and specificity of 81.58%. The optimal threshold of ΔADC_mean(%) for predicting tumor downstaging was 26.8%.

Fig. 1.

Patient A, stage IIIC1. (a) Before CCRT. (b) After CCRT, the tumor volume was reduced, but still had lymph node metastasis, re-staged as IIIC1, we defined as non-downstaging. PFS = 819 days, ΔADC_mean(%) = 25.8. Patient B, stage IIIC1. (c) Before CCRT. (d) After CCRT, the tumor volume was reduced and with no lymph node metastasis, re-staged as IIA1, we defined as downstaging. PFS = 1208 days, ΔADC_mean(%) = 28.2. CCRT, concurrent chemoradiotherapy; PFS, progression-free survival.

Fig. 2.

(a) Comparison of PFS between tumors downstaging and non-downstaging (b) ROC curves using ΔADC, ΔADC% mean, pre-treatment ADC values, and post-treatment ADC values to predict cervical cancer downstaging. ADC, apparent diffusion coefficient; PFS, progression-free survival; ROC, receiver operating characteristic.

Table 1.

Relationship between ADC value parameters and tumor downstaging.

Quantitative parameters	Downstaging group (n = 93)			Non-downstaging group (n = 28)			P value
Quantitative parameters		t	P		t	P
ΔADC_mean(%)	0.558 ± 0.347	16.232	<0.001	0.134 ± 0.286	2.885	0.007	< 0.001
ΔADC	389.374 ± 264.606	14.862	<0.001	95.374 ± 230.948	2.546	0.016	< 0.001
Pre-treatment ADC	758.729 ± 126.346	56.247	<0.001	826.216 ± 142.216	35.812	<0.001	0.171
Post-treatment ADC	1164.865 ± 260.353	44.527	<0.001	921.590 ± 213.519	26.607	<0.001	0.001

Values are given as mean ± SD unless otherwise indicated.

ADC, apparent diffusion coefficient.

ΔADC_mean(%) value predicts PFS and prognosis-related risk factors after CCRT treatment

A total of 121 patients were included in this study. The median follow-up time was 41.2 months (range = 18.2–78.5 months) and the median PFS time was 35.3 months (range = 5.4–78.5 months). During the follow-up period, 36 (29.8%) patients experienced tumor recurrence: locoregional recurrence in nine patients and distant metastasis in 26 patients, or both (Table 1). Using X-tile, it was discovered that when the corresponding ΔADC_mean(%) value was 40.8, the prognosis of the patients was significantly stratified (P < 0.001). The 121 patients were randomly divided into a training group (n = 81) and a validation group (n = 40) in a 2:1 ratio. Clinical and imaging parameters of the patients showed no statistically significant differences (P > 0.05) (Table 2). Kaplan–Meier curves indicated that patients can be significantly differentiated into low-risk and high-risk groups in the training group with ΔADC_mean(%) cutoff value determined by 40.8; the low-risk group had a lower recurrence rate and significantly longer PFS within 3 years (P < 0.001), which was validated in the validation group (P < 0.012) (Fig. 3). Univariate Cox regression analysis in the training group revealed that ΔADC_mean(%) (P < 0.01), pathological grade (P = 0.024), FIGO stage (P < 0.01), tumor diameter change rate (P < 0.01), pelvic lymph node metastasis (P < 0.01), and para-aortic lymph node metastasis (P < 0.01) were all high-risk factors for recurrence after CCRT treatment for cervical cancer. Multivariate analysis demonstrated that ΔADC_mean(%) (P < 0.01), pathological grade (P = 0.01), pelvic lymph node metastasis (P = 0.04), and para-aortic lymph node metastasis (P < 0.01) were all independent risk factors for predicting recurrence after CCRT treatment for cervical cancer (Tables 3 and 4). Independent risk factors were used to produce the prognostic nomogram model in Fig. 4. The ROC curves showed that in the training group, the AUC of ΔADC_mean(%) was 0.761 and the AUC of the nomogram model was 0.862; in the validation group, the AUC of ΔADC_mean(%) was 0.734 and the AUC of the nomogram model was 0.787 (Table 5). The results showed that the nomogram model constructed using ΔADC_mean(%) in combination with the clinical information was more effective in predicting 3-year PFS in patients with locally advanced cervical cancer, as depicted in Fig. 5.

Fig. 3.

Kaplan–Meier showed a significant difference in progression-free survival at 3 years between high- and low-risk groups in the training and validation groups when the ΔADC (%) value was 0.408.

Fig. 4.

ΔADC nomogram for predicting postoperative progression at 1–3 years after CCRT for cervical cancer. No pelvic or para-aortic LNMs were seen in this patient, the pathology graded was moderately differentiated, the ΔADC_mean(%) was 1.25, and the cumulative score corresponding to each component was 285, and the 3 years of PFS in this patient was 0.968. The gray area shows the distribution area. ADC, apparent diffusion coefficient; CCRT, concurrent chemoradiotherapy; LNM, lymph node metastasis.

Fig. 5.

Comparison of the decision curves of the ΔADC_mean(%) and ΔADC_mean(%)-nomogram in the training and validation groups.

Table 2.

Clinical prognostic factors of patients in training and validation groups.

Characteristic	Training group (n = 81)	Validation group (n = 40)	P value
Age (years)	58.2 ± 9.7	56.1 ± 16.4	0.362
Pathological type			0.239
Adenocarcinoma	3	4
SCC	76	36
Adenosquamous cell carcinoma	2	0
Grade			0.549
Low	14	10
Middle	64	28
High	3	2
FIGO stage			0.269
IB	7	3
IIA1	6	3
IIA2	4	7
IIB	42	14
IIIA	3	0
IIIB	1	1
IIIC1	9	7
IIIC2	2	2
IVA	7	3
Recur			0.147
Distant metastasis	16	10
Locoregional recurrence	4	5
Both	1	0
No Recur	60	25	0.19
SCC antigen (ng/mL)	7.337 ± 8.329	12.000 ± 18.489	0.058
NLR	3.109 ± 2.466	3.180 ± 2.086	0.876
MLR	0.379 ± 0.351	0.321 ± 0.187	0.330
Pre-treatment tumor size (cm)	4.173 ± 1.603	4.760 ± 1.625	0.062
Pre-treatment ADC (×10⁻³ mm²/s)	780.896 ± 143.488	769.230 ± 145.290	0.676
Post-treatment ADC (×10⁻³ mm²/s)	1129.795 ± 290.740	1057.893 ± 281.392	0.198
ΔADC_mean(%)	46.8 ± 39.8	42.6 ± 38.5	0.582
ΔADC_mean	348.898 ± 264.329	288.663 ± 310.359	0.268
Pelvic LNM (+)	17	13	0.170
Para-aortic LNM (+)	6	5	0.361
Parametrial invasion (+)	54	31	0.222

Values are given as n or mean ± SD. (+) means have this characteristic.

ADC, apparent diffusion coefficient; FIGO, International Federation of Gynecology and Obstetrics; LNM, lymph node metastasis; MLR, monocyte-lymphocyte ratio; NLR, neutrophil-lymphocyte ratio; SCC, squamous cell carcinoma.

Table 3.

Cox regression analysis of clinical and MRI variables for PFS.

MRI variable	Univariate			Multivariate
MRI variable	HR	95% CI	P value	HR	95% CI	P value
Grade	0.451	0.226–0.900	0.024	0.366	0.168–0.797	0.011
Pre-treatment tumor size	0.151	0.054–0.421	<0.001
FIGO stage	1.764	1.187–2.622	<0.001
ΔADC_mean(%)	0.144	0.058–0.355	<0.001	0.092	0.024–0.363	<0.001
Pelvic LNM (+)	2.443	1.259–4.743	0.008	2.743	1.030–7.310	0.044
Para-aortic LNM (+)	5.225	2.053–13.296	<0.001	4.552	1.683–12.312	0.003

ADC, apparent diffusion coefficient; FIGO, International Federation of Gynecology and Obstetrics; LNM, lymph node metastasis; MRI, magnetic resonance imaging; PFS, progression-free survival.

Table 4.

The ADC change values of the progressed and non-progressed in the training and validation groups.

ΔADC_mean(%)	Training group (n = 81)	Validation group (n = 40)	P value
Progress	0.228 ± 0.286	0.218 ± 0.366	0.306
No progress	0.552 ± 0.399	0.551 ± 0.346	0.444
P value	<0.006	0.001

Values are given as n or mean ± SD.

ADC, apparent diffusion coefficient.

Table 5.

Comparison of different models predicting 3-year PFS in the training and validation groups.

	ΔADC_mean(%)	ΔADC-nomogram
Training group AUC
1 year	0.710 (0.612–0.808)	0.782 (0.684–0.880)
2 years	0.747 (0.652–0.848)	0.851 (0.753–0.949)
3 years	0.761 (0.663–0.859)	0.862 (0.762–0.958)
Validation group AUC
1 year	0.722 (0.624–0.820)	0.880 (0.782–0.978)
2 years	0.701 (0.603–0.799)	0.801 (0.703–0.899)
3 years	0.734 (0.636–0.832)	0.787 (0.689–0.885)

Values in parentheses are 95% confidence intervals.

ADC, apparent diffusion coefficient; AUC, area under the receiver operating characteristic curve; PFS, progression-free survival.

Inter-reader reliability and variability

The inter-reader reliability and variability of tumor downstaging was 0.853 (95% CI = 0.800–0.893), and the reliability and variability of ΔADC_mean(%) was 0.954 (95% CI = 0.940–0.966).

Discussion

Our research results show that patients who were downstaged in tumor after CCRT had a significantly higher disease-free survival (DFS) compared to those who did not (P = 0.0039). When the ΔADC_mean(%) threshold was set at 26.8, the AUC value for the tumor restaging decrease was 0.868 (95% CI = 80.0–91.9%). In this study, the PFS of patients with cervical cancer was significantly stratified when the ΔADC_mean(%) threshold was 40.8, and the PFS of patients with lower ΔADC_mean(%) was significantly shorter than that of patients with a higher mean. ΔADC_mean(%), pathological grade, pelvic lymph node metastasis (P = 0.04), and para-aortic lymph node metastasis are independent risk factors affecting the PFS of patients with cervical cancer. A nomogram model using ΔADC (%) combined with clinical information yielded the optimal prediction for 3-year PFS with locally advanced cervical cancer (AUC = 0.862), which is higher than the prediction using ΔADC (%) alone (AUC = 0.761), and the same results were achieved in both the training group and the verification group.

CCRT can restore the immune equilibrium and improve the tumor immune microenvironment (12), making it a primary clinical treatment for locally advanced and advanced cervical cancer. Numerous studies (13–15) have shown that the tumor downstaging after neoadjuvant chemotherapy or CCRT therapy is considered to be a factor in the favorable prognosis of many tumors. Wang et al. (14) conducted a retrospective analysis of 226 patients with late-stage nasopharyngeal cancer who underwent CCRT treatment and found that patients with downstaging after CCRT had a significantly lower rate of distant metastasis (10.8%) compared to the non-downstaging group (22.9%) and exhibited significantly improved survival time, which was similar to our results. Han et al. (15) showed that preoperative CCRT treatment for rectal cancer can reduce T and N stages and achieve complete pathological remission. By predicting tumor response after CCRT and adjusting the treatment modality, the overall survival time can be prolonged. Although previous research has evaluated the relationship between tumor downstaging and prognosis, it did not comprehensively consider imaging parameters and failed to reflect changes in the molecular biology within the tumor. MRI has the advantages of non-invasive and high soft tissue resolution and is the first imaging method to evaluate the curative effect. ADC can quantify the movement of water molecules within cells, which is related to tumor density. Radiation and chemotherapy kill tumor cells and reduce their activity, altering water molecule diffusion. Prior research (6,16) has confirmed that ΔADC can reflect internal tumor changes before morphological alterations occur, which can be used to predict tumor regression rates and patient survival time after CCRT, assess treatment efficacy, monitor changes in disease status. However, no research has connected ΔADC_mean(%) with tumor downstaging after CCRT. Our study demonstrates a significant correlation between ΔADC_mean(%) and tumor downstaging.

Previous studies (17–19) have suggested that tumor size, clinical stage, lymph node metastasis, and SCC are prognostic factors related to cervical cancer outcomes, but they have not been combined with imaging parameters. Several studies (20–22) have now shown that ADC values can be used to predict the prognosis of patients with cervical cancer. Gu et al. (20). studied 124 patients and found that the ADC value before treatment had no clear relationship with tumor progression and that ΔADC_mean(%) was an independent risk factor for prognosis, and ΔADC (%), pelvic lymph node metastasis, and type of pathology were imaging-independent risk factors for prognosis, which is consistent with our study. However, this study concluded that neither squamous carcinoma antigen nor histopathological type was an independent risk factor for tumor progression, which is inconsistent with the findings of Gu et al. One consideration regarding our study is that only 9 (7.4%) patients with non-squamous carcinoma were included, which was significantly fewer than the 21 (16.9%) patients in that study. Previous studies (23,24) with large sample sizes have demonstrated that cervical cancer survival is closely related to the status of lymph node metastasis, and patients with concomitant lymph node metastasis have a lower overall survival rate. Our study confirmed that para-aortic and pelvic lymph node metastasis is an independent risk factor for prognosis, which is consistent with the studies of Gouy et al. and Chen et al. (25,26). Himoto et al. (19) showed that ΔADC_mean(%) and tumor volume change rate are high-risk factors for predicting the recurrence of cervical cancer, but tumor volume change was not an independent risk factor for prognosis, which is consistent with the results of our study. A previous study (27) also found that different tumor volumes affect the prognosis of patients with cervical cancer, but multivariate regression analysis in our study showed that pretreatment tumor size was not an independent risk factor for prognosis, and Zhang et al.'s (28) study reached with the same conclusion. It is considered that although the tumor size can directly express the extent of tumor involvement, it cannot reflect the degree of internal invasion of the tumor. The relationship between pre-treatment ADC value and tumor prognosis in CCRT for cervical cancer is highly controversial, and several previous studies (29–31) have shown that the pre-treatment ADC value is also a sensitive factor for assessing the risk of progression of cervical cancer. Due to various factors affecting the ADC value before treatment, such as changes in the tumor microenvironment, it may not be an effective marker for evaluating prognosis, our study is consistent with the findings of recent years.

Due to the heterogeneity of tumor cells and individual differences, there is clinical variation in the efficacy of CCRT. Lee et al. (32) found that increasing the treatment dose for specific tumor populations while keeping the total treatment duration can effectively improve patient prognosis. Therefore, we need to evaluate its efficacy at an early stage and adjust the course and dose of radiotherapy appropriately, to reduce the postoperative recurrence rate of patients and prolong the length of DFS. Our study helps to screen groups with therapeutic advantages, develop individualized protocols, and improve prognosis.

The present study has some limitations. First, our study is a single-center retrospective analysis, which is prone to selection bias, but it is internally validated and the conclusions are consistent between groups. Second, different pathological types of cervical cancer have different ADC values. This factor was not examined because the number of patients included with non-squamous cancers is small. Third, our study constructs a generalized model and does not target specific subgroups. It is hoped that the sample will be expanded for targeted analysis in the future. Fourth, the study time nodes were 1 month after CCRT; however, a study (33) reported that the optimal window to reflect the efficacy of CCRT is 2 weeks after the start of CCRT. Further studies are needed to determine the optimal time window for MRI-ADC examination for early prediction of clinical outcome.

In conclusion, our results demonstrated that ΔADC_mean(%) is an effective predictor of tumor downstaging. Nomograms incorporating clinical factors and ΔADC_mean(%) hold significant clinical value in predicting downstaging and PFS in patients with cervical cancer after CCRT, offering a valuable reference for prognostic assessment and personalized treatment strategies.

Supplemental Material

sj-doc-1-acr-10.1177_02841851241283042 - Supplemental material for Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Supplemental material, sj-doc-1-acr-10.1177_02841851241283042 for Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer by Jiawei Fan, Wenfei Li, Mengyu Cheng, Zhehan Wang, Zhanqiu Wang, Tao Chen and Tao Gu in Acta Radiologica

Footnotes

Acknowledgments

Thanks to all who participated in the publication of this study.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study is supported by the Science and Technology Program of Qinhuangdao (Project Number: 202401A114 by Wenfei Li).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on the reasonable request.

ORCID iD

Wenfei Li

Supplementary material

Supplementary material for this article is available online.

References

Salvo

Odetto

Pareja

, et al. International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging: a review of gaps and questions that remain. Int J Gynecol Cancer 2018;30:873–878.

Wang

, et al. Neoadjuvant chemotherapy with weekly cisplatin and paclitaxel followed by chemoradiation for locally advanced cervical cancer. BMC Cancer 2023;23:51.

Attademo

Tuninetti

Pisano

, et al. Immunotherapy in cervix cancer. Cancer Treat Rev 2020;90:102088.

Wang

. Small cell (neuroendocrine) carcinoma of the cervix: an analysis for 19 cases and literature review. Front Cell Infect Microbiol 2022;12:916506.

Tewari

Sill

Penson

, et al. Bevacizumab for advanced cervical cancer: final overall survival and adverse event analysis of a randomised, controlled, open-label, phase 3 trial (gynecologic oncology group 240). Lancet 2017;390:1654–1663.

Haldorsen

Lura

Blaakær

, et al.

What is the role of imaging at primary diagnostic work-up in uterine cervical cancer?

Curr Oncol Rep 2019;21:77.

Kido

Nakamoto

. Implications of the new FIGO staging and the role of imaging in cervical cancer. Br J Radiol 2021;94:20201342.

Wang

Zhou

, et al. The value, diagnostic efficacy and clinical significance of functional magnetic resonance imaging in evaluating the efficacy of neoadjuvant chemotherapy in patients with triple negative breast cancer. Front Oncol 2023;13:1132186.

Zhang

Liu

, et al. Multiparametric liver MRI for predicting early recurrence of hepatocellular carcinoma after microwave ablation. Cancer Imaging 2022;22:42.

10.

Liu

Wang

Zhang

, et al. Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment. Abdom Radiol (NY) 2022;47:3327–3237.

11.

Lin

Yang

Lin

, et al. Prognostic model based on magnetic resonance imaging, whole-tumour ADC values and HPV genotyping for stage IB-IV cervical cancer patients following chemoradiotherapy. Eur Radiol 2019;29:556–565.

12.

Liu

Yin

, et al. The dynamic alternation of local and systemic tumor immune microenvironment during concurrent chemoradiotherapy of cervical cancer: a prospective clinical trial. Int J Radiat Oncol Biol Phys 2021;110:1432–1441.

13.

Dai

Leu

, et al. Neoadjuvant chemoradiotherapy and larynx-preserving surgery for cervical esophageal cancer. J Clin Med 2020;9:387.

14.

Wang

Peng

, et al. Association of tumor downstaging after neoadjuvant chemotherapy with survival in patients with locally advanced nasopharyngeal carcinoma: a retrospective cohort study. J Cancer Res Clin Oncol 2021;147:2913–2922.

15.

Han

Kim

Lee

, et al. Neoadjuvant therapy of metformin is associated with good tumor response after preoperative concurrent chemoradiotherapy for rectal cancer. Sci Rep 2022;12:3716.

16.

Allen

Bhosale

, et al. Diffusion-weighted magnetic resonance imaging as a predictor of outcome in cervical cancer after chemoradiation. Int J Radiat Oncol Biol Phys 2017;97:546–553.

17.

Liu

Wang

, et al. Development and validation of novel nomograms to predict the overall survival and cancer-specific survival of cervical cancer patients with lymph node metastasis. Front Oncol 2022;12:857375.

18.

Ding

Chen

, et al. Prognostic models for predicting overall and cancer-specific survival of patients with initially diagnosed metastatic cervical squamous cell carcinoma: a study based on SEER database. Medicine (Baltimore) 2023;28:e34313.

19.

Watanabe

Nakamura

Ichikawa

, et al. Early alteration in apparent diffusion coefficient and tumor volume in cervical cancer treated with chemoradiotherapy or radiotherapy: incremental prognostic value over pretreatment assessments. Radiother Oncol 2021;155:3–9.

20.

Kim

Choi

, et al. Prognostic value of ADC quantification for clinical outcome in uterine cervical cancer treated with concurrent chemoradiotherapy. Eur Radiol 2019;11:6236–6244.

21.

Zhou

Zhang

, et al. Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer. Eur Radiol 2022;4:2540–2551.

22.

Zhang

Liu

Zhang

, et al. Nomograms combining clinical and imaging parameters to predict recurrence and disease-free survival after concurrent chemoradiotherapy in patients with locally advanced cervical cancer. Acad Radiol 2023;30:499–508.

23.

Machida

Matsuo

Kobayashi

, et al. Significance of histology and nodal status on the survival of women with early-stage cervical cancer: validation of the 2018 FIGO cervical cancer staging system. J Gynecol Oncol 2022;33:e26.

24.

Van Kol

KGG

Ebisch

RMF

Van Der

AAM

, et al. The prognostic value of the presence of pelvic and/or para-aortic lymph node metastases in cervical cancer patients; the influence of the new FIGO classification (stage IIIC). Gynecol Oncol 2023;171:9–14.

25.

Gouy

Morice

Narducci

, et al. Prospective multicenter study evaluating the survival of patients with locally advanced cervical cancer undergoing laparoscopic para-aortic lymphadenectomy before chemoradiotherapy in the era of positron emission tomography imaging. J Clin Oncol 2013;31:3026–3033.

26.

Chen

Sun

, et al. Prognostic value of apparent diffusion coefficient in neuroendocrine carcinomas of the uterine cervix. PeerJ 2023;11:e15084.

27.

Xue

Ren

Yang

, et al. Histogram analysis of apparent diffusion coefficient for the assessment of local aggressiveness of cervical cancer. Arch Gynecol Obstet 2014;290:341–348.

28.

Zhang

Zhao

Zhang

, et al. MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy. Cancer Imaging 2022;22:35.

29.

Gladwish

Milosevic

Fyles

, et al. Association of apparent diffusion coefficient with disease recurrence in patients with locally advanced cervical cancer treated with radical chemotherapy and radiation therapy. Radiology 2016;279:158–166.

30.

Himoto

Fujimoto

Kido

, et al. Pretreatment mean apparent diffusion coefficient is significantly correlated with event-free survival in patients with International Federation of Gynecology And Obstetrics Stage Ib to IIIb cervical cancer. Int J Gynecol Cancer 2015;25:1079–1085.

31.

Holopainen

Lahtinen

Könönen

, et al. Greater increases in intratumoral apparent diffusion coefficients after chemoradiotherapy predict better overall survival of patients with cervical cancer. PLoS One 2023;18:e0285786.

32.

Lee

Jeong

Lee

, et al. High-dose (60 Gy) intensity-modulated radiotherapy with concurrent weekly cisplatin followed by intracavitary radiation in locally advanced cervical cancer: a phase II prospective clinical trial. Gynecol Oncol 2023;177:142–149.

33.

Liu

Sun

Bai

, et al. Time-window of early detection of response to concurrent chemoradiation in cervical cancer by using diffusion-weighted MR imaging: a pilot study. Radiat Oncol 2015;10:185.

Supplementary Material

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Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Abstract

Background

Purpose

Material and Methods

Results

Conclusion

Keywords

Introduction

Material and Methods

Patients

Image analysis

MRI method

CCRT protocol

Clinical and histopathologic factors

Clinical outcomes

Statistical analysis

Results

Baseline characteristics

ΔADCmean(%) value predicts tumor downstaging value

ΔADCmean(%) value predicts PFS and prognosis-related risk factors after CCRT treatment

Inter-reader reliability and variability

Discussion

Supplemental Material

sj-doc-1-acr-10.1177_02841851241283042 - Supplemental material for Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Footnotes

Acknowledgments

Declaration of conflicting interests

Funding

Data availability

ORCID iD

Supplementary material

References

Supplementary Material

ΔADC_mean(%) value predicts tumor downstaging value

ΔADC_mean(%) value predicts PFS and prognosis-related risk factors after CCRT treatment