Objectives
Target therapy with BRAF/MEK inhibitors in metastatic melanoma is characterised by a high response rate; however, acquired resistance to treatment develops in many cases. We aimed to ...investigate if baseline total metabolic tumour volume (TMTV) and therapy-response assessment by
18
FFDG PET/CT have a prognostic role on progression-free survival (PFS) and overall survival (OS) in patients with metastatic melanoma receiving BRAF ± MEK inhibitors.
Methods
Fifty-seven patients who performed an
18
FFDG PET/CT at baseline and on treatment were retrospectively evaluated. A Cox proportional-hazard model was used to examine associations between OS and PFS with baseline clinical/PET parameters as well as for PET response.
Results
According to EORTC criteria, 34 patients were classified as responders (partial/complete metabolic response PMR/CMR) and 23 as non-responders (progressive/stable metabolic disease PMD/SMD). Baseline characteristics associated with a shorter PFS were more than two metastatic organ sites and TMTV > 56 cm
3
; the latter was the only independent feature at multivariate analysis. Patients achieving a CMR were associated with a prolonged PFS compared with those with PMR (median PFS 42.9 vs 8.8 months;
p
= 0.009). Disease progression occurred in new-onset disease sites in 87.5% of CMR, 7.1% of PMR and 34.8% of PMD/SMD (
p
< 0.001). High baseline TMTV and lack of treatment response were independent prognostic factors for OS, stratifying patients in three different prognostic classes (median OS 6.7, 18.3 and 102.2 months, respectively).
Conclusions
Baseline TMTV and metabolic response may be useful prognostic indicators for PFS and OS in patients with advanced melanoma treated with BRAF/MEK inhibitors.
Key Points
• In a retrospective cohort of 57 metastatic melanoma patients treated with BRAF/MEK inhibitors, a TMTV > 56 cm
3
at baseline
18
FFDG PET/CT was significantly correlated with a shorter PFS and OS.
• The combined use of baseline TMTV along with PET response during treatment allowed for the identification of three groups of patients with very different median OS.
Objective
The application of
18
FFDG PET/CT in predicting histologic response to induction chemotherapy in patients with Ewing sarcoma (EWS) has been proposed using the values of pre-post treatment ...SUV
max
as a referral parameter, although with heterogeneous results. The aim of this retrospective study was to evaluate the diagnostic accuracy of
18
FFDG PET/CT volumetric parameters (metabolic tumour volume (MTV) and total lesion glycolysis (TLG)) as compared to SUV
max
to predict response to chemotherapy and clinical outcome in patients with localised EWS of bone and soft-tissue.
Methods
Twenty-eight patients with non-metastatic EWS of bone (
n
= 20) and soft tissues (
n
= 8) who underwent a
18
FFDG PET/CT scan before (PET
1
) and after induction chemotherapy (PET
2
) were enclosed in the analysis. Values of PET metrics (SUV
max
, MTV, TLG) at diagnosis and after neoadjuvant chemotherapy as well as the percentage change between PET
1
and PET
2
(ΔSUV, ΔMTV and ΔTLG) were correlated to histological response and to progression-free survival (PFS).
Results
ΔTLG (cut-off: -60%) is the best predictor for histologic response with 100% sensitivity and 77.8% specificity. MTV
1
> 33.4 cm
3
and TLG
1
> 112 were also associated with a favourable histologic response (sensitivity 80% and specificity 77.8% for both). On multivariate analysis, SUV
2
(> 3.3) and ΔTLG (< -18%) were independent predictors of worse PFS.
Conclusions
18
FFDG PET/CT could accurately predict histologic response to neoadjuvant chemotherapy in patients with EWS, also showing a possible prognostic value for future disease relapse.
Key Points
•
The variation of the PET parameter tumour lesion glycolysis (TLG) can predict the histologic response to induction chemotherapy (sensitivity 100%, specificity 77.8%), in patients with Ewing sarcoma.
•
The percentage variation of TLG and the value of the SUVmax at PET scan after chemotherapy show a prognostic role for future disease relapse. The combination of both the parameters identifies three prognostic classes of patients with low, intermediate and high risk of disease relapse.
Giant cell tumour of bone (GCTB) is a benign, locally aggressive primary bone neoplasm that represents 5% of all bone tumours. The principal treatment approach is surgery. Although generally GCTB is ...considered only a locally aggressive disease, it can metastasise, and lung metastases occur in 1–9% of patients. To date, only the use of denosumab has been approved as medical treatment for GCTB. Even more rarely, GCTB undergoes sarcomatous transformation into a malignant tumour (4% of all GCTB), but history of this malignant transformation is unclear and unpredictable. Considering the rarity of the event, the data in the literature are few. In this review, we summarise published data of GCTB malignant transformation and we analyse three cases of malignant transformation of GCTB, evaluating histopathology, genetics, and radiological aspects. Despite the rarity of this event, we conclude that a strict follow up is recommended to detect early malignant transformation.
The combination of BRAF and MEK inhibitors represents the standard of care treatment for patients with metastatic
-mutated melanoma, notwithstanding the high frequency of emergent resistance. ...Moreover, therapeutic options outside clinical trials are scarce when patients have progressed after both targeted therapy and therapy with immune checkpoint inhibitors. In this article, we report our experience with targeted therapy rechallenging with BRAF and MEK inhibitors in patients with metastatic
-mutated melanoma after progression with kinase inhibitors and immunotherapy.
Four patients with metastatic
-mutated melanoma were rechallenged with BRAF and MEK inhibitors after progression with targeted therapy and subsequent immunotherapy (checkpoint inhibitors).
Two patients (one of them was heavily pretreated) had partial response over 36 months (with local treatment on oligoprogression disease) and 10 months, respectively. A third patient with multisite visceral disease and high serum levels of lactate dehydrogenase had a short-lived clinical benefit rapidly followed by massive progression of disease (early progressor). The fourth patient, currently on treatment with BRAF/MEK inhibitors, is showing a clinical benefit and radiological stable disease over 3 months of therapy. Adverse events were manageable, similar to those reported during the first targeted therapy; the treatment was better tolerated at rechallenge compared with the first treatment by two out of four patients.
The addition of cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) to endocrine therapy impressively improved the outcome of patients with hormone receptor-positive metastatic breast cancer. Despite ...their great efficacy, not all patients respond to treatment and many of them develop acquired resistance. The aim of this retrospective study was to assess the role of 18F-FDG PET/CT in predicting PFS and OS in breast cancer patients treated with CDK4/6i.
114 patients who performed an 18F-FDG PET/CT scan before (PET1) and 2-6 months (PET2) after starting treatment were retrospectively enrolled. Metabolic response was evaluated by EORTC, PERCIST and Deauville Score and correlated to PFS and OS.
In patients who did not progress at PET2 (n = 90), PFS rates were not significantly different between classes of response by EORTC and PERCIST. Conversely, patients showing a Deauville score ≤3 had a longer PFS (median PFS 42 vs 21.0 months; p = 0.008). A higher total metabolic tumor volume at PET1 (TMTV1) was also associated with a shorter PFS (median 18 vs 42 months; p = 0.0026). TMTV1 and Deauville score were the only independent prognostic factors for PFS at multivariate analysis and their combination stratified the population in four definite classes of relapse risk. Conversely, the above parameters did not affect OS which was only influenced by a progressive metabolic disease at PET2 (3-years survival rate 29.8 vs 84.9%; p<0.0001).
TMTV and metabolic response by Deauville score were significant prognostic factors for PFS in patients with breast cancer treated with CDK4/6i. Their determination could help physicians to select patients who may need a closer follow up.
Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed ...tomography (CT) radiomics-based machine learning for classification of atypical cartilaginous tumours and higher-grade chondrosarcomas of long bones.
One-hundred-twenty patients with histology-proven lesions were retrospectively included. The training cohort consisted of 84 CT scans from centre 1 (n=55 G1 or atypical cartilaginous tumours; n=29 G2-G4 chondrosarcomas). The external test cohort consisted of the CT component of 36 positron emission tomography-CT scans from centre 2 (n=16 G1 or atypical cartilaginous tumours; n=20 G2-G4 chondrosarcomas). Bidimensional segmentation was performed on preoperative CT. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, the performance of a machine-learning classifier (LogitBoost) was assessed on the training cohort using 10-fold cross-validation and on the external test cohort. In centre 2, its performance was compared with preoperative biopsy and an experienced radiologist using McNemar's test.
The classifier had 81% (AUC=0.89) and 75% (AUC=0.78) accuracy in identifying the lesions in the training and external test cohorts, respectively. Specifically, its accuracy in classifying atypical cartilaginous tumours and higher-grade chondrosarcomas was 84% and 78% in the training cohort, and 81% and 70% in the external test cohort, respectively. Preoperative biopsy had 64% (AUC=0.66) accuracy (p=0.29). The radiologist had 81% accuracy (p=0.75).
Machine learning showed good accuracy in classifying atypical and higher-grade cartilaginous tumours of long bones based on preoperative CT radiomic features.
ESSR Young Researchers Grant.
Objectives
Cartilaginous bone tumors represent a wide variety of neoplasms ranging from benign to extremely aggressive malignant lesions. Unlike other tumors, the biopsy cannot easily predict the ...histological grade, sometimes not allowing choosing the best therapeutic approach. The aim of the study was to evaluate the ability of
18
F-FDG PET/CT to differentiate enchondroma from chondrosarcoma and to predict the histological grade as compared to biopsy.
Methods
18
F-FDG PET/CT of 95 patients with chondroid lesions were retrospectively evaluated. The best SUV
max
cutoff to predict the post-surgical histological grade were correlated to those of biopsy and to several radiologic aggressiveness features, which were summarized in the parameter “Radiologic Aggressiveness Score” (AgSCORE).
Results
A concordance between the preoperative biopsy and the definitive histological grade was observed overall in 78.3% of patients, the lowest accuracy (58.6%) being in the identification of intermediate/high-grade chondrosarcoma (G2/G3). The best SUV
max
cutoff was 2.6 to discriminate enchondroma vs. low-grade chondrosarcoma (sensitivity 0.68, specificity 0.86), 3.7 to differentiate low-grade vs. intermediate/high-grade chondrosarcoma (sensitivity 0.83, specificity 0.84) and 7.7 to differentiate intermediate/high-grade vs. dedifferentiated chondrosarcoma (sensitivity 0.92, specificity 0.9). The AgSCORE also showed a high accuracy to differentiate between G1 and G2/G3 chondrosarcoma (cutoff = 4; sensitivity 0.76; specificity 0.89). An even higher accuracy was observed in those cases in which both SUV
max
and AgSCORE cutoff were concordant.
Conclusions
Results in this large series of patients suggest a potential role of
18
F-FDG PET/CT for histological grading of cartilaginous tumors, thus helping the orthopedic surgeon towards the most appropriate surgical procedure.
The extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility ...and machine learning prediction of response to neoadjuvant chemotherapy.
This retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient >0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation.
1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy.
Compared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier.
Purpose
To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.
Material and ...methods
This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort.
Results
Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (
p
= 0.474).
Conclusion
MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.
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