https://orcid.org/0000-0001-8303-2531
ABSTRACT
Objective: To investigate the relationship between 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) related parameters and the prognosis of multiple myeloma and to establish and validate a prediction model regarding the progression-free survival (PFS) of multiple myeloma.
Methods: A retrospective analysis of 126 newly diagnosed multiple myeloma patients who attended Nanjing Drum Tower Hospital from 2014–2021. All patients underwent PET/CT before treatment and were divided into a training cohort (n = 75) and a validation cohort (n = 51). Multivariate Cox proportional hazard regression analysis incorporated PET/CT-related parameters and clinical indicators. A nomogram was established to individually predict PFS in MM patients. The model was evaluated by calculating the C-index and calibration curve.
Results: Here, 4.2 was used as the cut-off value of SUVmax to divide patients into high and low groups. PFS significantly differed between patients in the high-SUVmax group and low-SUVmax group, and SUVmax was an independent predictor of PFS in newly diagnosed multiple myeloma (NDMM) patients. Univariate and multivariate cox regression analysis suggested that lactate dehydrogenase (LDH), bone marrow plasma cell (BMPC), and SUVmax affected PFS. These factors were incorporated to construct a nomogram model for predicting PFS at 1 and 2 years in NDMM patients. The C-index and calibration curves of the nomogram exhibited good accuracy and consistency, and the DCA curves suggested that the model had good clinical utility.
Conclusion: The PET/CT parameter SUVmax is closely related to the prognosis of myeloma patients. The nomogram constructed in this study based on PET/CT-related parameters and clinical indicators individually predicts the PFS rate of NDMM patients and enables further risk stratification of NDMM patients.
KEYWORDS:
Introduction
Multiple myeloma (MM) is a hematologic tumor characterized by malignant clonal appreciation of plasma cells. MM is currently the second most prevalent hematologic tumor with clinical features mainly manifesting as renal impairment, hypercalcemia, anemia, and osteolytic changes [Citation1]. Although the use of proteasome inhibitors, immunomodulators, monoclonal antibodies, nuclear export protein inhibitor drugs, and novel therapeutic modalities, such as CAR-T and hematopoietic stem cells, have shown significant improvements in overall survival (OS) and progression-free survival (PFS) in MM [Citation2], the prognosis of myeloma patients remains highly heterogeneous. Therefore, accurate and individualized risk assessment is crucial for treatment decisions and prognosis assessment. Currently, the most commonly used prognostic systems for patients with MM include DS staging, ISS staging, R-ISS staging [Citation3–5], and the Mayo Consensus Guidelines for Myeloma Stratification and Risk-Adjusted Therapy (mSMART), which places a better emphasis on cytogenetics [Citation6–8]; however, these systems for assessing prognosis are not perfect. More convenient and sensitive prognostic stratification systems are still under investigation. With the widespread use of imaging technologies, such as CT, MRI, SPECT-CT, and PET/CT, in recent years, clinicians can better diagnose and evaluate the assessment of patients with MM [Citation9].
18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is a new novel imaging technique that combines CT anatomical imaging and metabolic information to detect myeloma bone and extramedullary lesions more sensitively than traditional imaging techniques [Citation10, Citation11]. In addition, 18F-FDG PET/CT is able to assess patient prognosis and response to treatment and detect microscopic residual lesions (MRD) within and outside the bone marrow [Citation12]. Many previous studies have shown that 18F-FDG uptake in skeletal lesions can indicate tumor metabolic activity and that maximum uptake values of bone lesions (SUVmax) and hypermetabolic focal lesions (FL) exhibit prognostic significance in NDMM patients [Citation13, Citation14]. However, the optimal cut-off values for the number of lesions and SUVmax remain unclear. There are relatively few studies in which the parameters of PET/CT are used in combination with other clinical indicators to predict the prognosis of NDMM patients. Combining PET/CT parameters can improve clinicians’ individualized treatment and determine MM patient prognosis. Nomograms have a higher value of use as a model map with the intuitive prediction of individualized prognosis. Various indicators can be employed to estimate the incidence of each patient at certain times, so the nomogram has been widely used as an essential tool to predict the prognosis of various cancers [Citation15].
Therefore, we explored the relationship between PET/CT-related parameters and the clinical characteristics and prognosis of NDMM patients and constructed a nomogram for PFS prediction of MM by combining PET/CT parameters with clinical indicators. Then, the model was further validated internally.
Material and methods
Patients
We retrospectively analyzed clinical data of patients with MM diagnosed from 2014–2021 in the Department of Hematology, Nanjing Drum Tower Hospital, Nanjing, China. These patients met the IMWG diagnostic criteria, and treatment response was assessed in these patients. The inclusion criteria were as follows: 1. Patients with a definite diagnosis of MM by laboratory or bone marrow pathology, 2. Patients with PET/CT before the first treatment, 3. Patients with complete clinical data available. The exclusion criteria were as follows: 1. Patients with missing clinical data, 2. Patients with poor fusion images on PET/CT, 3. Patients lost to follow-up. PFS calculation started from the initial diagnosis of MM until the patients experienced disease progression or death, or the end of the follow-up.
Data collection
The clinical characteristics and demographics of 126 NDMM patients were collected by reviewing the patient's medical charts. The following information was obtained: age at diagnosis, immunoglobulin subtype, platelets (PLT), lactate dehydrogenase (LDH), albumin (ALB), creatinine, blood calcium levels, hemoglobin, bone marrow plasma cell (BMPC) percentage, DS stage, ISS stage, SUVmax of PET/CT, number of focal lesions, first-line therapy regimens and other laboratory data. Baseline PET/CT was performed before treatment, and the images were reviewed by two experienced nuclear medicine physicians unaware of the initial clinical information and the reference standard outcome.
PET/CT scan protocol
In advance of treatment, an 18F-FDG PET/CT of the whole body was performed using the Gemini GXL PET/CT scanner with the 16-slice CT (Philips Corp, Netherlands). Patients fasted for at least 6 h and were intravenously administered 185–370 MBq of 18F-FDG (5.18 MBq/kg). The patient’s current blood glucose levels were measured before 18F-FDG administration. All patients were weighed before each scan to calculate the standardized uptake value (SUV). A PET/CT scan of the patient's whole body (head to upper thigh) was performed 60 min after intravenous injection of 18F-FDG. The following CT scan parameters were employed: 80 milliampere seconds (mAs) and 150 kilovolts peak (kVp). The slice thickness was 3.75–5 mm. PET imaging was then acquired in three-dimensional mode for 2-min emission acquisitions per field of view. PET images were reconstructed as a 128 × 128 matrix using CT attenuation correction and the ordered-subset maximum expectation iterative reconstruction algorithm. Through the use of Syntegra software, the PET images and CT images were image registered and fused. The positive PET/CT findings were characterized by focal areas of heightened tracer uptake within bones, either with or without underlying lesions on CT over at least two consecutive slices. Alternatively, a SUVmax ≥ 2.5 within osteolytic CT areas > 1.0 cm in size, or a SUVmax ≥ 1.5 within osteolytic CT areas ≤ 1.0 cm, also defined positive findings. Two PET/CT physicians reviewed the film to obtain data on the location, number, SUVmax, and extramedullary myeloma lesions.
Data analysis
In total, 126 NDMM patients meeting inclusion criteria were randomly divided into training and validation sets according to a 6:4 ratio. The cardinality test and Fisher's exact test were used to compare the distribution of the underlying characteristics between the two sets. Receiver operating characteristic (ROC) curves were used to estimate the optimal cut-off value of SUVmax on PFS at the lesion. Significant independent risk factors (P < 0.1) identified by univariate analysis were further included in the multivariable Cox proportional hazards regression to identify independent risk factors and construct the nomogram to prequalify MM patients for PFS at 1 and 2 years. Time ROC was constructed to validate the discrimination of the model, and calibration curves were used to assess the consistency of the model. Decision curve analysis (DCA) was used to assess the clinical benefit of the model. In addition, we calculated PFS scores for each patient by the nomogram and divided the patients into two different risk groups using ROC curves.
Survival curves were plotted by the Kaplan-Meier method. The log-rank test was used to compare survival trends between the high- and low-risk groups, and a two-tailed P < 0.05 was defined as statistically significant. All statistical analyses were performed using IBM SPSS Statistics version 26.0 (SPSS Inc., Chicago, IL, USA) and R software version 4.0.3 (HTTP://www.r-project.org).
Results
Baseline characteristics of the population
A total of 126 patients ultimately met the inclusion and exclusion criteria. Among these eligible patients, the mean age was 64.1 ± 7.4 years (range: 40–85 years), and 71 (56.3%) participants were aged ≥ 65 years. The mean value of SUVmax in the whole cohort was 5.7 ± 4.2. The ROC curve was established by analyzing the relationship between SUVmax and PFS. Here, SUVmax = 4.2 was identified as the critical value. In total, 69 patients (54.8%) had SUVmax ≥ 4.2, and 57 patients (45.2%) had < 4.2. The number of lesions was detected by PET/CT. The number of lesions was greater than or equal to 4 in a total of 75 (59.5%) patients. Based on the inclusion and exclusion criteria, a total of 126 individuals were included in the analysis. In total, 75 patients were included in the training cohort and 51 in the validation set. There were no statistically significant differences in age, sex, DS stage, ISS stage, hemoglobin, PLT, ALB, creatinine, β2-MG, SUVmax, the type of bone disease, immunoglobulin subtype, whether or not ASCT was performed, and first-line therapy regimens were noted between the two groups ().
Table 1. Patients’ baseline characteristics.
Relationship between SUVmax and clinical parameters
The median PFS time for all patients was 23 months (range: 3–84 months), with 25 months for the training set (range: 3–84 months) and 18 months for the validation set (range: 4–80 months) (P = 0.259). The median follow-up time for the study was 38 months (range: 3–84 months), with 39 months for the training cohort (range: 3–84 months) and 38 months for the validation cohort (range: 4–80 months) (P = 0.617). We compared patients with SUVmax ≥ 4.2 (n = 69) with those with SUVmax < 4.2 (n = 57) and found significant differences in DS staging, blood calcium levels, BMPC, the number of bone lesions, and type of bone lesions between the high- and low-SUVmax groups (P < 0.05) (). In the overall cohort, the PFS rate was better in the low-SUVmax group than in the high-SUVmax group (2-year PFS rate: 73.9% vs. 30.3%, P < 0.001). In the training cohort, the PFS rate was better in the low-SUVmax group compared with the high-SUVmax group (2-year PFS rate: 74.5% vs. 31.9%, P < 0.001). In the validation cohort, the low-SUVmax group had a better PFS rate than the high-SUVmax group (2-year PFS rate: 67.9% vs. 27.9%, P = 0.0039) ().
Figure 1. The Kaplan-Meier curves of SUVmax ≥ 4.2 and positive SUVmax < 4.2 group. The PFS curves for patients in training cohort (A), validation cohort (B), and overall cohort (C).
Table 2. The relationship between SUVmax and clinical characteristics.
Construction and validation of the nomogram
In the training cohort, hemoglobin < 110 g/L, PLT < 150, LDH ≥ 245 U/L, blood calcium levels ≥ 2.75 mmol/L, BMPC ≥ 20%, and SUVmax ≥ 4.2 were found to be independent predictors of PFS by Cox univariate analysis (p < 0.1), and these factors were included in multivariate Cox analysis (P < 0.05). Multivariate Cox analysis demonstrated that LDH ≥ 245 U/L, BMPC ≥ 20%, and SUVmax ≥ 4.2 remained independent prognostic factors for PFS (). However, the number of focal lesions was not an independent prognostic factor for MM, nor were clinical indicators such as ALB and creatinine. Therefore, we constructed a nomogram of 1,2-year PFS for NDMM patients based on multivariate analysis of independent prognostic factors in the training cohort ().
Figure 2. Nomogram of PFS for patients with NDMM.
Table 3. Univariate and multivariate Cox regression analysis of clinical risk factors associated with PFS in the development cohort.
The C-index (concordance index) of PFS was 0.749 (95% CI: 0.692–0.816) for the training cohort and 0.717 (95% CI: 0.637–0.797) for the validation cohort, suggesting that the constructed nomogram exhibits good discrimination. Subsequently, we plotted the ROC curves of the 1-year and 2-year PFS values for the training and validation sets.
The AUC values for predicting PFS at 12 and 24 months in the training cohort were 0.789 and 0.798, respectively, and the AUC values for predicting PFS at 12 and 24 months in the validation cohort were 0.742 and 0.745, respectively. ROC analysis showed that the AUC values of the nomogram were significantly higher than those of the conventional staging systems, including the DS and ISS staging systems, and prognostic indicators ().
Figure 3. The time-ROC curves of the nomogram and other characteristics to predict 1- and 2-year PFS in the training cohort (A, B), validation cohort(C, D).
The calibration curves generated by 1000 bootstrap resampling indicate the agreement between the nomogram predictions and the actual probabilities. The calibration curves in the training and validation sets showed good agreement between the nomogram predictions and the actual PFS probabilities, indicating that the model exhibits high accuracy in predicting PFS ().
Figure 4. Calibration curves for predicting 1- and 2-year PFS in training cohort (A, B), validation cohort (C, D).
In addition, we plotted DCA curves of 1- and 2-year PFS to show the best net benefit of the nomogram, which also demonstrated that the prediction model possesses better higher net benefits than the ISS stage (). The PFS score of each NDMM patient was calculated from the nomogram, and the maximum Youden index through the ROC curve was obtained. We selected 114.9 as the cut-off value for the total PFS score, and overall population risk stratification was performed based on the total score to classify the patients into high-risk and low-risk groups. The survival curves in the overall population, validation and training sets suggested that high-risk individuals had a worse prognosis than the low-risk group (). These results indicate that the nomogram we constructed can successfully stratify patients with MM based on prognostic risk.
Figure 5. Decision curve of the nomograms and ISS stage in predicting 1- and 2-year PFS in the training cohort (A, B), validation cohort(C, D).
Figure 6. Kaplan–Meier survival curves of PFS showing the risk levels determined using the nomogram in the training (A), validation cohort (B), and overall cohort (C).
Discussion
MM is a disease with a mostly incurable clinical course and significant heterogeneity. Although tremendous progress has been made in treating MM, patients will eventually progress, relapse or even die. Therefore, a more accurate predictive method must be developed to improve the PFS of MM. This study aimed to investigate the feasibility of establishing a prognostic nomogram for MM based on PET/CT combined with clinical indicators. The nomogram we established demonstrated better predictive accuracy than traditional DS and ISS staging, with C-index of 0.749 and 0.717 for the training and test sets, respectively. Predictive models regarding MM prognosis based on cytokine levels and MRI imaging features have been developed in previous studies [Citation16, Citation17], but relatively few models based on PET/CT have been reported. The current prognostic value of PET/CT examination in NDMM patients has been described in a previous study [Citation18]. In the present retrospective analysis of 126 patients with MM who underwent PET/CT, we confirmed that SUVmax, LDH, and BMPC were independent predictors of PFS in patients with MM and that the level of SUVmax correlated with the clinical parameters and patient prognosis. In previous studies, all these factors were associated with the prognosis of MM. LDH strongly correlates with prognosis in other tumors, such as lymphoma and solid tumors [Citation19, Citation20].
High serum LDH levels have been reported in many studies to be a marker of poor prognosis and short survival in myeloma and have been included in the R-ISS system [Citation21]. Our results support LDH as an independent predictor of PFS in patients with NDMM [Citation22]. BMPC is used as an index to assess the proliferation of monoclonal proliferators, which is also a parameter to distinguish smoldering myeloma from active myeloma. We also confirmed that BMPC is an independent prognostic factor for PFS in NDMM patients, which is consistent with previous findings [Citation23, Citation24]. However, for example, age, hemoglobin, PLT, β2-mg, and ASCT, which represent common prognostic factors for MM in other studies, were not associated with prognosis in our study. This difference may be caused by the limited amount of data [Citation25]. SUVmax is associated with some properties in other medium tumors, and a high SUVmax significantly correlated with tumor proliferation behavior and tumor load in other studies [Citation26].
In addition, high SUVmax has been associated with higher risk cytogenetics, such as del(17p). Specifically, del(17p) is a high-risk trait for myeloma and has been included in the R2-ISS scoring system [Citation27, Citation28]. The cut-off value for SUVmax is not yet definitive. Differences in cut-off values may be attributed to residuals in the patient population, testing technique, and the clinician’s level of experience. Previous studies have demonstrated that SUVmax > 4.2 is associated with a lower PFS [Citation14]. Numerous other studies have shown that a higher SUVmax is associated with a poor prognosis for MM despite the choice of different cut-off values [Citation29, Citation30]. However, most current reports are single-center reports, and it is currently unclear whether these cut-off values can be generally applied to the prognosis of NDMM. When we selected the same cut-off value of 4.2 for SUVmax based on the time ROC curve, we found that the high-SUVmax group had a high DS stage and elevated blood calcium levels, BMPC, increased bone destruction, and a worse PFS rate.
Park's study showed that FL > 3 was a significant prognostic factor for OS/PFS. However, univariate analysis in our cohort showed that FL > 3 was not significant for PFS (P > 0.05) and did not show a correlation between the number of bone destructions at the premises and prognosis. This divergent finding compared to previous studies is possible due to selection bias or the small sample size [Citation31].
Regarding the conventional analysis of traditional staging, DS staging and ISS staging showed poor predictive ability in our cohort. DS staging mainly indicates the tumor load of MM and still exhibits significant limitations in predicting the prognosis of MM. ISS staging lacks cytogenetic indicators in prognostic assessment, which is similarly disadvantageous [Citation32].
The nomogram is a fast, intuitive, and accurate tool to predict individual PFS [Citation33], and the risk stratification method based on the nomogram has shown good performance in predicting individualized PFS quantification in patients with NDMM. Therefore, we developed a nomogram to predict PFS in MM patients using PET/CT imaging parameters and clinical indicators. The nomogram demonstrated better discrimination than DS staging, ISS staging, and the clinical and imaging parameters of PET/CT models.
Furthermore, SUVmax was incorporated as a new predictor and combined with LDH and BMPC to generate a prediction model. With the advent of the new drug era in MM, there is also a need for more individualized and accurate risk stratification to facilitate more effective treatment of high-risk patients [Citation34]. Herein, an individualized treatment approach can be adopted for MM patients in different risk strata to improve PFS. Our results showed significant differences in PFS between the low-risk and high-risk groups in the overall training cohort and validation cohort, so the nomogram provided well-differentiated risk stratification. The nomogram and further risk stratification based on PET/CT can be applied more easily and noninvasively in clinical practice.
Although the established nomogram demonstrated good accuracy in predicting 1- and 2-year PFS in the NDMM patient cohort we studied, some limitations of this study should be noted. First, given that the data used in this study were obtained from a single center, we did not have rigorous external validation, and subsequent external data is needed to validate our established nomogram. Not all prognostic indicators related to MM were included. For example, cytogenetic deletions can lead to some deviation from the actual situation, leading to our inability to analyze the R-ISS and Mayo staging systems. The PET/CT also includes parameters such as total glucose digestion value, tumor metabolic volume, and imaging histology-related features [Citation35]. The role of PET/CT in predicting MM prognosis should be further explored in the future. In addition, our study was retrospective, and further prospective studies are needed for future validation.
In conclusion, we studied the relationship between SUVmax and prognosis in NDMM patients who underwent PET-CT examination. The nomogram constructed by combining PET/CT features and clinical indicators can predict the PFS rate more accurately and individually in NDMM patients.
Ethical approval
This study was approved by the institutional review board of Nanjing Drum Tower Hospital (number: 2023-043-01). The requirement for informed consent was waived owing to the retrospective design of the study.
Availability of data
The data used and analyzed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors have declared that no competing interest exists.
Author contributions
B. Chen, P. Xu and R. Wang designed the study; X. Dong and R. Wang performed the statistical analysis and drafted the manuscript; J. Xu participated in patient selection and implementation of the study; X. Dong, Y. Peng, X. Ying and J. Yan collected and provided patient data; X. Dong and Y. Peng revised the manuscript. B. Chen and Y. Peng approved the final revision.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
References
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