Introduction: Functional high risk (HR) multiple myeloma (MM) patients (pts) (namely, pts in whom the disease progresses within 12 to 18 months despite an optimal initial therapy) represent an unmet clinical need despite the availability of highly effective treatments. Diffusion-weighted whole-body MRI (DW-MRI) is increasingly used in the management of MM pts due to its high sensitivity and information regarding response to treatment. The Myeloma Response Assessment and Diagnosis System (MY-RADS) recommendations have established criteria for Response Assessment Category (RAC) with a 5-point scale defining the range of imaging response after treatment from complete (RAC 1) to progressive disease (RAC 5). Furthermore, the RAC score appears to be able to independently stratify pts with different outcomes after autologous stem cell transplantation (ASCT). More recently, a growing interest is emerging in the evaluation of relative fat fraction (rFF) obtainable from MRI, which provides additional insights into bone disease status. Response to treatment of bone disease increases the rFF through a fatty marrow reconversion in previously active lesions, but data regarding the optimal rFF threshold predictive of outcome in MM are lacking. Aim of this study was to evaluate if the combined assessment of RAC score and rFF could better identify functional HR pts after ASCT.

Patients and methods: We retrospectively analyzed the outcome of MM pts newly diagnosed at our institution from January 2018 to December 2022 and who underwent DW-MRI evaluation after ASCT, before maintenance. Post ASCT progression free survival (PFS) was calculated from the day of ASCT (the second one in case of double ASCT) until progression or death from any cause. In addition to the RAC score, the rFF of up to 5 target lesions was evaluated for each patient and pts outcome was analyzed accordingly. ROC curve analysis was performed in order to select the optimal threshold value for rFF predictive for early relapse, defined in our study as clinical relapse within 18 months after ASCT. Marrow MRD evaluation before maintenance was also performed with 8-color FCM (sensitivity 10-5).

Results: Out of 97 pts recorded, 33 (34%) were ISS stage 3 and 34 (35%) showed HR cytogenetics. Their median age was 61 years. Pts were treated with the following induction regimens: VTD 58, VRD 6, Dara-VRD 5, Dara-VCD 6, Dara-VTD 21, Isa-KRD 1. Single ASCT with MEL200 conditioning was performed in 56 pts (58%), whereas 41 pts (42%) received double ASCT. Response rates before maintenance were PR 5%, VGPR 33%, CR 43% and sCR 19%. Of 79 pts with available MRD result, 52 (66%) were negative. A complete imaging response was observed in 66 (RAC 1: 68%). A total of 416 areas on DW-MRI before maintenance were drawn and reviewed to calculate rFF values. After a median follow up of 47 months, post ASCT PFS was significantly longer in pts with RAC 1 vs RAC ≥2 (median NR vs 24.6 months, p 0.0009, HR 0.27; 95%CI 0.13-0.58), as well as post ASCT OS (3-year rate 95% vs 73%, p 0.0072, HR 0.24; 95%CI 0.07-0.81).

Mean rFF ROC analysis revealed an AUC of 0.84 (95% CI 0.72-0.95, p <0.0001); cut-off threshold of 17.2% for rFF identified early relapse with 83% (95% CI 0.61-0.94) sensitivity, 85% (95% CI 0.75-0.90%) specificity, a PPV of 55% (95% CI 36-74%) and a NPV of 96% (95% CI 91-100%). Post ASCT PFS was significantly superior in pts with rFF >17.2% vs rFF <17.2%, defined below as rFF High vs rFF Low: median NR vs 13.7 months, p < 0.0001, HR 0.18 (95% CI 0.08-0.43), as well as post ASCT OS (3- year rate 96% vs 62%, p < 0.0001, HR 0.12; 95% CI 0.03-0.45). Combining RAC score and rFF, post ASCT PFS was significantly better for patients with RAC 1/rFF High before maintenance, compared to pts with RAC ≥2/rFF Low (median NR vs 12.3 months, p < 0.0001, HR 0.21; 95% CI 0.07-0.62). Intermediate PFS (42 months) was observed for pts with either RAC ≥2/rFF High or RAC1/rFF Low, with a significantly different outcome of the three subgroups (p < 0.0001). With a threshold of 17.2%, high or low rFF before maintenance independently affected post ASCT PFS and OS on a multivariate analysis.

Conclusion: rFF can complement the RAC score in the assessment of response after ASCT, representing a powerful tool allowing for early identification of functional HR MM pts with early relapse and particularly poor prognosis.

Disclosures

Belotti:GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Menarini Stemline: Membership on an entity's Board of Directors or advisory committees. Cattaneo:Pfizer: Other: travel grant; Jazz: Other: travel grant; JANSSEN: Other: travel grant. Roccaro:Transcan2/EraNet/FRRB: Research Funding; Fondazione AIRC: Research Funding; EHA: Research Funding; AstraZeneca: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Tucci:Kiowa Kyrin: Membership on an entity's Board of Directors or advisory committees; Lilly: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Gentili: Membership on an entity's Board of Directors or advisory committees.

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2024
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