Predictive value of PET metabolic parameters for occult lymph node metastases in PET/CT defined node-negative patients with advanced epithelial ovarian cancer

doi:10.21203/rs.3.rs-2693892/v1

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Predictive value of PET metabolic parameters for occult lymph node metastases in PET/CT defined node-negative patients with advanced epithelial ovarian cancer

https://doi.org/10.21203/rs.3.rs-2693892/v1

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published 09 Jun, 2023

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Accurate lymph node metastasis (LNM) prediction is crucial for patients with advanced epithelial ovarian cancer (AEOC) since it guides the decisions about lymphadenectomy. Many previous studies showed that occult lymph node metastasis was common in AEOC. The objective of our study is to quantitatively assess the probability of occult lymph node metastasis (OLNM) defined by 18F-Fluorodeoxyglucose (FDG) PET/CT in AEOC and explore relationship between ONNM and PET metabolic parameters. The patients with pathologically confirmed AEOC who underwent PET/CT for preoperative staging at our institute were reviewed. Univariate and multivariate analyses were performed to evaluate the predictive value of PET/CT-related metabolic parameters for OLNM. The result of our study showed metastatic TLG index had a better diagnostic performance than other PET/CT-related metabolic parameters. Two variables were independently and significantly associated with OLNM in multivariate analysis: metastatic TLG index and primary tumor location. The logistic model combining metastatic TLG index and primary tumor location might be a promising tool to effectively predict the individualized possibility of OLNM for AEOC patients.

Biological sciences/Biological techniques/Imaging/Positron emission tomography

Biological sciences/Cancer/Gynaecological cancer

Ovarian carcinoma is one of the most deadly gynecologic cancer because the majority of patients are diagnosed with advanced-stage (Stages III and IV) according to the International Federation of Gynecology and Obstetrics (FIGO) staging classification¹. Epithelial ovarian cancer (EOC) is the most crucial histological subtype, accounting for approximately 70% of ovarian cancers^2,3. LN status has an important impact on the FIGO stage of EOC^4,5. Although debulking surgery is the basic treatment of AEOC, pelvic and para-aortic lymphadenectomy is not part of the debulking surgery for all patients⁶. No benefit in overall survival or progression-free survival was shown between cytoreductive surgery with and without pelvic and para-aortic lymphadenectomy, suggesting that lymphadenectomy should be omitted for those patients without LNM^7,8. Identification of metastatic LN in patients with AEOC before debulking surgery is important since it guides the decisions about lymphadenectomy.

The frequency of LNM observed in EOC is up to 75% of patients with stage III–IV and 25% of patients with stage I–II ^8,9. LNM has been evaluated using several non-invasive modalities of preoperative imaging, such as computed tomography (CT), magnetic resonance imaging (MRI), and 2-[¹⁸F] fluoro-2-deoxy-D-glucose (¹⁸F-FDG) PET/ CT^10–13. Clinical OLNM is defined as no suspicion of lymph node involvement on CT or PET images¹⁴. In prior studies, OLNM was mainly defined according to CT modality. However, PET/CT, which can provide the functional and anatomical information of malignancy, is considered the most accurate LNM staging modality^15–17. Although preoperative PET/CT showed higher diagnostic accuracy than CT in detecting pelvic and para-aortic LNM in AEOC, the sensitivity of PET/CT was unsatisfactory, ranging from 26.7–87.6% ^18–20. Low diagnostic sensitivity of PET/CT indicated clinical OLNM was common for AEOC, which accounted for approximately 20–80% of patients^10,16,18.

Therefore, the challenge is to distinguish the patients with OLNM who should have a lymphadenectomy from the patient with “no suspect lymph nodes” who should not have lymphadenectomy. Previous studies have demonstrated that certain PET/CT parameters, such as standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), were significant prognostic factors for AEOC^21–25. However, there was no study to evaluate the correlation between PET/CT-related parameters and LNM, especially OLNM. Previous studies have demonstrated that hypermetabolic LN on PET/CT, initial CA125 ≥ 500, and initial peritoneal cancer index ≥ 10 were independently and significantly associated with pelvic and/or para-aortic LNM^26,27. Recently, many researchers have focused on radiomics features of PET/CT or contrast-enhanced CT in predicting LNM in AEOC, among which LNM assessment has been proven to have a certain significant improvement^28,29. However, the manually time-consuming delineation of ROI and complex models limit its clinical application. The objective of our study is to evaluate the relationship between PET/CT-related parameters and OLNM and construct a simple model to predict OLNM.

Patients. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This retrospective study was approved by the Medical Ethics Committee of Shengjing Hospital of China Medical University (2020PS374K). Informed consent was not required by these oversight committees due to the retrospective nature of the study.

From January 2013 to December 2022, a total of 777 consecutive patients with pathologically confirmed EOC who underwent ¹⁸F-PET/CT for preoperative staging at our institute were reviewed. Inclusion criteria were as follows: (1) received debulking surgery with pelvic and para-aortic lymphadenectomy; (2) postoperative pathological examination confirmed stage III or IV EOC with definite LN status; (3) PET/CT scan was performed within one month before surgery; The exclusion criteria included the following: (1) received any neoadjuvant chemotherapy (NAC) before surgery; (2) poor image quality or image data loss; (3) negative uptake of the primary or metastatic lesions in PET/CT images (SUVmax < 2.5).

The PET/CT images of patients who met the inclusion criteria were evaluated by two radiologists (with more than five years of experience in abdominal imaging in PET/CT images) to confirm whether they had positive LN on PET/CT images. Further analyses were performed for the patients with no positive LN on PET/CT. Then those patients were divided into OLNM group (with pathologically confirmed LNM) and NLNM group (with no pathologically confirmed LNM). The flowchart of patient selection is shown in Figure. 1. Clinical data such as age, menopause status, CA125 and so on were collected from the hospital information system of our hospital. The ascites and location of primary tumor was evaluated by previous radiologists on PET/CT scans. The ascitic volume was divided into two categories: small (< 1500ml) and large amount of ascitic (≥ 1500mml), according to ascitic volume assessed on CT component of PET/CT³⁰. FDG avid in one side or two side tubo-ovarian was defined as unilateral or bilateral involvement.

LN status assessment. Our study defined LNM as one or more pelvic or para-aortic lymph node metastases confirmed from the postoperative pathological results. The preoperative LN status on PET/CT images of enrolled patients were evaluated by two radiologists (Fig. 2). Inconsistent results reached consensus through consultation by two radiologists. OLNM patients were defined as patients in whom there was no suspicion of lymph node involvement neither on CT (short-axis diameter < 1 cm) nor on PET (standardized uptake value < 2.5).

¹⁸ F-FDG PET/CT Image Acquisition. Whole-body ¹⁸F-FDG PET/CT scans were performed by a dedicated PET/CT scanner (Discovery 690, GE Healthcare, Milwaukee, WI) within one month before any treatment. Patients fasted for at least 6 hours were injected with 3.7 MBq/kg ± 10% of body weight ¹⁸F-FDG. Then the acquisition was performed after approximately 60 min. The 3D ordered subset expectation maximization (OSEM) algorithm with attenuation correction was used for the reconstruction of the PET image. The voxel sizes of the reconstructed PET image were 3.65×3.65×3.27 mm³, and the matrix size was 192×192. The CT scans (109 mA, 140 kV) were performed before the PET scan with a matrix size of 512×512 and a voxel size of 0.98×0.98×3.75 mm³.

Measurement of PET metabolic parameters. Finally, PET/CT images were transferred into 3D Slicer software (version 4.8.1, https://www.slicer.org/) to extract PET metabolic parameters. Primary and metastatic lesions were delineated with an extension of 3D Slicer called PETTumorSegmentation, which could provide an editor-Effect for semi-automated segmentation of tumors and hot lymph nodes in PET scans ³¹. A combined CT and PET scan reading was performed to avoid including the physiologic uptake in the volume of interest. PET-related metabolic parameters were calculated with PET-IndiC, an extension of 3D Slicer. The parameters measured in primary and all lesions were as follows: SUVmean, SUVmax, MTV, TLG, and SUVpeak. The MTV and TLG of metastatic lesions were calculated by difference between primary and all lesions. The proportion of MTV and TLG of metastatic lesions in all lesions were defined as metastatic MTV index and metastatic TLG index.

Statistical analysis. Student t-tests or Mann–Whitney U tests were used for continuous variables, and Chi-squared tests and exact Fisher tests were applied for categorical variables between two groups. The association between variables and OLNM was evaluated by univariate logistic regression analysis. The features with a P-value < 0.05 in the univariate logistic regression analysis were selected for multivariate logistic regression analysis. The multivariate logistic regression analysis with the optimal subset method was used to assess the best combination of variables that were independently associated with OLNM. The ROC curve was used to assess the performance of the final logistic model. All analyses and calculations were done with R (version 4.0.5, www.Rproject.org). A p-value < 0.05 was considered statistically significant.

Patient selection and clinical characteristics. Finally, 496 patients with primary AEOC fulfilling all inclusion criteria were included in our study. Three hundred and twenty-one patients with no positive LN on PET/CT images evaluated by radiologists were divided into the OLNM group(n = 155) and the NLNM group(n = 165). OLNM accounted for 51.3% of all LNM and 48.3% of negative LN on PET/CT scans. The characteristics of the patients for further analysis are shown in Table 1. In the OLNM group, 48 patients were associated with both pelvic and para-aortic LNM, 86 patients only had pelvic LNM, and 21 patients only had para-aortic LNM. There was no significant difference in the number of LN resected in cytoreductive surgery, 20.434 ± 10.543 in the NLNM group and 21.987 ± 12.210 in the OLNM group. The age, initial HE4, initial 724, and menopausal state showed no statistically significant between two groups.

Tale 1 Patients Characteristics

Characteristics	TLNM group n = 166	OLNM group n = 155	Total	P-value
Age	55.825 ± 9.660	54.245 ± 9.271	55.062 ± 9.493	0.136
Ascites				0.005
- Small	111 (66.867%)	79 (50.968%)	190 (59.190%)
- Large	55 (33.133%)	76 (49.032%)	131 (40.810%)
HE4	524.233 ± 405.548	618.708 ± 409.960	569.851 ± 409.784	0.039
CA199	67.658 ± 168.730	33.129 ± 81.675	50.985 ± 134.865	0.019
CA724	43.803 ± 72.517	63.302 ± 91.523	53.219 ± 82.692	0.036
CA125	1067.805 ± 1264.763	1461.316 ± 1479.211	1257.818 ± 1384.413	0.011
Pathological type				0.069
- HGSOC	136 (81.928%)	139 (89.677%)	275 (85.670%)
- Others	30 (18.072%)	16 (10.323%)	46 (14.330%)
Menopausal state				0.453
- No	41 (24.699%)	45 (29.032%)	86 (26.791%)
- Yes	125 (75.301%)	110 (70.968%)	235 (73.209%)
Resected LN numbers	20.434 ± 10.543	21.987 ± 12.210	21.184 ± 11.387	0.223
Resected LN stations	0.000 ± 0.000	4.000 ± 4.218	1.931 ± 3.545	< 0.001
Location				0.001
- Unilateral	51 (30.723%)	22 (14.194%)	73 (22.741%)
- Bilateral	115 (69.277%)	133 (85.806%)	248 (77.259%)
Primary lesions SUVmean	6.409 ± 2.603	6.394 ± 4.145	6.402 ± 3.430	0.969
Primary lesions SUVmax	15.786 ± 7.022	15.267 ± 13.879	15.535 ± 10.872	0.676
Primary lesions MTV	174.514 ± 163.401	128.518 ± 130.195	152.304 ± 149.848	0.005
Primary lesions TLG	1196.512 ± 1326.612	821.808 ± 871.071	1015.580 ± 1143.579	0.003
Primary lesions SUVpeak	12.936 ± 6.067	11.947 ± 8.602	12.458 ± 7.405	0.238
All lesions SUVmean	6.193 ± 2.497	5.814 ± 2.079	6.010 ± 2.309	0.14
All lesions SUVmax	17.497 ± 11.131	17.226 ± 17.489	17.366 ± 14.530	0.87
ALL lesion MTV	311.310 ± 255.478	385.207 ± 292.735	346.992 ± 276.156	0.016
ALL lesion TLG	2016.447 ± 2019.289	2347.499 ± 2181.457	2176.301 ± 2102.398	0.159
ALL lesion SUVpeak	13.824 ± 6.860	19.437 ± 54.441	16.535 ± 38.190	0.204
Metastatic lesions MTV	136.792 ± 232.498	251.595 ± 264.120	192.226 ± 254.451	< 0.001
Metastatic MTV index	0.355 ± 0.351	0.577 ± 0.341	0.462 ± 0.363	< 0.001
Metastatic lesions TLG	819.936 ± 1512.754	1478.419 ± 1774.855	1137.895 ± 1674.685	< 0.001
Metastatic TLG index	0.334 ± 0.351	0.559 ± 0.343	0.443 ± 0.364	< 0.001

3.2 Predictive performance of PET metabolic parameters. The results of univariate logistic regression analysis of PET-related metabolic parameters showed MTV of primary lesions, TLG of primary lesions, MTV of all lesions, MTV of metastatic lesions, metastatic MTV index, and metastatic TLG index were significantly associated with OLNM. In ROC curve analysis, AUC of metastatic TLG index (0.684, 95%CI 0.645–0.760) was larger than other PET-related metastatic parameters (Fig. 3). We found that there was severe collinearity between PET-related metabolic parameters with p < 0.05 in univariate analysis. The correlation between those parameters was assessed by Pearson correlation analysis (Table 2).

Table 2

Pearson correlation of the PET/CT parameters with p < 0.05 in univariate analysis
Variables	Primary lesions MTV	Primary lesions TLG	ALL lesion MTV	Metastatic lesions MTV	Metastatic MTV index	Metastatic lesions TLG	Metastatic TLG index
Primary lesions MTV	1.000	0.849	0.352	-0.217	-0.560	-0.163	-0.556
Primary lesions TLG	0.849	1.000	0.345	-0.142	-0.444	-0.038	-0.457
ALL lesion MTV	0.352	0.345	1.000	0.824	0.343	0.802	0.348
Metastatic lesions MTV	-0.217	-0.142	0.824	1.000	0.692	0.941	0.695
Metastatic MTV index	-0.560	-0.444	0.343	0.692	1.000	0.615	0.990
Metastatic lesions TLG	-0.163	-0.038	0.802	0.941	0.615	1.000	0.623
Metastatic TLG index	-0.556	-0.457	0.348	0.695	0.990	0.623	1.000

3.3 Risk factors and prediction model. Among all clinical characteristics, the volume of ascites, CA125, and location of the primary tumor were significantly correlated with a high risk of OLNM (p < 0.05). In order to eliminate the correlation and multiple linearities between the variables, particularly PET-related metabolic parameters, multivariate logistic regression with an optimum subset method was utilized to simplify the financial indexes. The findings from the univariate and multivariate logistic analysis are presented in Table 3. The metastatic TLG index (OR = 4.740, 95% CI 2.453–9.320) and location of primary tumors (OR = 1.930, 95%CI 1.074–3.531) were significantly associated with OLNM (p < 0.05). The results are displayed using forest plots (Fig. 4). The ROC-AUC of the final logistic model after bootstrap replicates 1000 times was 0.702 (95%CI 0.645–0.759) (Fig. 5a). The predicted and observed probabilities of OLNM is shown in Fig. 5b and did not differ significantly (p = 0.06).

Table 3

Univariate and multivariate logistic analysis for OLNM
Varibles	Univariate analysis		Multivariate analysis
Varibles	OR(95%CI)	P	OR(95%CI)	P
Age	0.982(0.959, 1.005)	0.137
Ascites (Small/Large)	1.941(1.239, 3.059)	0.004
CA199	0.997 (0.995, 0.999)	0.036
HE4	1.000(0.999, 1.000)	0.314
CA724	1.002(1.000, 1.005)	0.053
CA125	1.000(1.000, 1.000)	0.012	1.000(1.000–1.000)	0.104
Menopausal state (Yes/No)	0.802(0.488, 1.315)	0.381
Pathological (HGSOC/Others)	0.521(0.266, 0.988)	0.05
Location (Unilateral/Bilateral)	2.681(1.550, 4.761)	0.001	1.930(1.074–3.531)	0.03
Primary lesions SUVmean	0.998(0.931, 1.068)	0.968
Primary lesions SUVmax	0.995(0.970, 1.016)	0.673
Primary lesions MTV	0.997(0.996, 0.999)	0.007
Primary lesions TLG	0.999(0.999, 0.999)	0.005
Primary lesions SUVpeak	0.980(0.942, 1.011)	0.264
All lesions SUVmean	0.928(0.834, 1.023)	0.148
All lesions SUVmax	0.998(0.981, 1.014)	0.867
ALL lesions MTV	1.001(1.000, 1.001)	0.019
ALL lesions TLG	1.000(0.999, 1.000)	0.163
ALL lesions SUVpeak	1.004(0.996, 1.025)	0.451
Metastatic lesions MTV	1.004(1.002, 1.007)	< 0.001
Metastatic MTV index	5.801(3.069, 11.19)	< 0.001
Metastatic lesions TLG	1.001(0.999, 1.004)	0.156
Metastatic TLG index	5.908(3.129, 11.39)	< 0.001	4.740(2.453–9.320)	< 0.001

The state of LN in patients with AEOC affects the choice of lymphadenectomy and prognosis³². To the best of our knowledge, this was the first clinical study to assess occult pelvic and para-aortic LNM in patients with AEOC after cytoreductive surgery. In our study, the proportion of clinical OLNM was high, accounting for 51.3% of all LNM and 48.3% of negative LN on PET/CT scans. The primary tumor location and metastatic TLG index extracted from TLG of metastatic and all lesions were significantly associated with OLNM. The final logistic model could preoperatively predict OLNM in AEOC.

The use of systematic lymphadenectomy is controversial for patients with EOC. Although the positive rate of lymph nodes in patients with presumed early-stage EOC was low, appropriately 3% − 14% detected by systematic lymphadenectomy, LNM in AEOC was high^4,5,33. LNM was found in 60.9% of all patients in our study, similar to the previous study ⁹. Occult nodal involvements were frequently found in AEOC. In our study, only 48.75% of the cases with LNM had positive lymph nodes on PET/CT scans. The other 51.25% of cases were negative in PET/CT scans. ¹⁸F-PET/CT is the most accurate modality for preoperative tumor staging and detection of metastatic lymph nodes. A high proportion of LNM is negative in PET/CT scans due to the low spatial resolution of PET-CT images and the low tissue uptake of the ¹⁸FDG. Most of the metastatic lesions, including LNM, tents to uptake less fluorodeoxyglucose than the primary lesions. Furthermore, tumor FDG binding depends on histological type and has a low affinity in mucus and clear cell adenocarcinoma³⁴. In addition, the micro-metastases will not be detected because of the low reconstructed spatial resolution of PET. The previous study demonstrated sensitivity for detecting metastatic lesions 4 mm or less in short-axis diameter was 12.5%, that for between 5 and 9 mm was 66.7%³⁵. Finally, massive ascites and extensive peritoneal metastasis also impact the assessment of LNM. In summary, OLNM is frequently observed in AEOC, which leads to the low sensitivity of ¹⁸F-PET/CT for LNM evaluation.

Many previous studies demonstrate that it was feasible to improve the diagnostic efficiency in LNM by clinical factors, radiological findings, and radiomics features extracted from PET or CT images. A new diagnostic tool based on multivariate analysis including three variables: pelvic and/or para-aortic LNM on CT PET/CT, and initial PCI ≥ 10, initial CA125 ≥ 500 was proposed²⁷. Another study used preoperative radiological scores to predict pelvic and/or para-aortic LNM²⁸. Although the ROC-AUC of those prediction models was moderate, the preoperative assessment of PCI / or colon involvement was difficult and ambiguous. Multiple studies have demonstrated the value of radiomics features in predicting the LNM of EOC^28,36. The radiomics signatures extracted from CT images incorporating LN reports by radiologists could improve the diagnostic efficiency in LNM³⁶. However, the calculation of the radiomics features is very complicated and time-consuming. We also found that the evaluation of LNM mainly relied on the subjective assessment of radiologists in previous studies. It is easy to identify positive LN on PET/CT scans because of hypermetabolism of metastatic LN. Identification of OLNM is critical for improving sensitivity in detecting LNM.

Previous studies have investigated the predictive value of PET metabolic parameters in AEOC^25,27,37. MTV and TLG were independent prognostic factors for disease progression and overall survival after cytoreductive surgery in patients with EOC^23,25. In our study, MTV, TLG, metastatic TLG index, and metastatic MTV index were highly associated with OLNM. The metastatic TLG and MTV indexes can quantify EOC's metastatic capacity, which positively correlates with metastatic tumor burden. OLNM, as part of metastatic lesions, was closely associated with soakage and metastasis of malignant ovarian tumors. Therefore, the metastatic TLG index was the only independent predictive factor in both univariate and multivariate and had a higher predictive value than other PET-related parameters. Bilateral ovarian cancers were associated with a high risk of OLNM because bilateral tumors showed more tumor heterogeneity and invasiveness. The final multivariate model incorporated clinical factors, radiological findings, and composite Index extracted from PET metabolic parameters.

There are several limitations to the present study. Firstly, this study is a single center retrospective analysis, potentially suffering from its inherent selection bias and limiting data generalizability. All patients underwent PET/CT scans in our hospitals using the same devices. The resolution of the image, administration of a carbohydrate free diet before scanning, and time for uptake were inevitably different, which might result in heterogeneous results. Further multicenter study is necessary to prove the efficiency of the result of this study. Secondly, the patients with neoadjuvant chemotherapy before surgery were not included in our study because NAC affects the pathological evaluation of postoperative LN status. NACT was considered for patients with advanced-stage ovarian cancer who are not good candidates for primary debulking surgery due to advanced age, frailty, poor performance status, comorbidities, or who have disease unlikely to be optimally cytoreduced^38,39. Further study will be needed to explore OLNM in patients who received NACT before primary cytoreductive surgery. Thirdly, our study focused on patients with negative LN on PET/CT scans. Only the patients with negative reports in PET/CT scans were included in our study. Comparison between OLNM and pathologically confirmed no LNM, regardless of positive LN on PET/CT, will be needed in further study.

In conclusion, our study demonstrated that OLNM defined by PET/CT was frequently observed for patients with AEOC. The metastatic TLG index extracted from metastatic and all hot lesions was a significant predictor for OLNM. The logistic model, by combining metastatic TLG index, MTV of metastatic lesions, CA125, and primary tumor location, is a promising tool to improve the diagnostic performance of OLNM. Hence, this simple method may provide valuable information for triaging patients according to simple lymphadenectomy decision rules.

Acknowledgements

Not applicable.

Author contributions

Data collection: XB; Data analysis: XB; Manuscript writing: HW; Manuscript review: HW. All the authors read and approved the final manuscript.

Funding

Not applicable

Availability of data and materials

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

Ethics approval and content to participate

Ethics approval was granted by the ethics committee of the Shengjing Hospital of China Medical University.

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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published 09 Jun, 2023

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Predictive value of PET metabolic parameters for occult lymph node metastases in PET/CT defined node-negative patients with advanced epithelial ovarian cancer

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