Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study
Abstract
:1. Background
2. Material and Methods
2.1. Workflow Framework
2.2. Patient Selection
2.3. Imaging and Metastases Segmentation
2.4. Feature Extraction and Machine-Learning Model
2.5. Model Evaluation
3. Results
3.1. Demographics, Best Response, Progression-Free Survival for Six Months, and Overall Survival after Six and Twelve Months
3.2. Prediction of Best Response, PFS, and Overall Survival
3.2.1. Model Evaluation
3.2.2. Risk Stratification for Overall Survival
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ALM | acral lentiginous melanoma |
AUC | area under the curve |
BRAF | v-Raf murine sarcoma viral oncogene homolog B1 |
CI | confidence interval |
CMMR | Central Malignant Melanoma Registry |
CR | complete response |
CT | computed tomography |
CTLA-4 | cytotoxic T-lymphocyte-associated protein 4 |
CV | cross-validation |
FCBF | fast correlation-based filter for feature selection |
IQR | interquartile range |
LDH | lactate dehydrogenase |
LMM | lentigo maligna melanoma |
LoG | Laplacian of Gaussian |
MEK | mitogen-activated protein kinase |
ML | machine learning |
mRMR | minimum redundancy maximum relevance feature selection |
n | number |
NM | nodular melanoma |
OS | overall survival |
PACS | picture archiving and communication system |
PD | progressive disease |
PD-1 | programmed death 1 |
PET | positron emission tomography |
PFS | progression-free survival |
PR | partial response |
RAF | rapidly accelerated fibrosarcoma |
RAS | rat sarcoma |
RECIST | response evaluation criteria in solid tumors |
ROC | receiver operating characteristic |
SD | stable disease |
SSM | superficial spreading melanoma |
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Clinical Data | ||
---|---|---|
Age (years) [median, (IQR)] | 56 (21.5) | |
Gender (female) [n, %] | 37 (41%) | |
Localization of primary tumor [n, %] | Head/neck | 11 (12%) |
Torso | 39 (43%) | |
Upper extremity | 5 (5%) | |
Lower extremity | 19 (21%) | |
n/a | 17 (19%) | |
Histological subtype [n, %] | SSM | 50 (55%) |
NM | 31 (34%) | |
LMM | 1 (1%) | |
ALM | 1 (1%) | |
n/a | 8 (9%) | |
BRAF V600E mutation status [n, %] | BRAF wildtype | 0 (0%) |
BRAF mutation | 91 (100%) | |
Baseline LDH [n, %] | Normal (<250 U/L) | 33 (36%) |
Elevated (≥250 U/L) | 42 (46%) | |
n/a | 16 (18%) | |
Baseline S100B [n, %] | Normal (<0.1 µg/L) | 23 (25%) |
Elevated (≥0.1 µg/L) | 51 (56%) | |
n/a | 17 (19%) | |
Number of metastatic organs [n, %] | 1–3 | 70 (77%) |
>3 | 21 (23%) | |
Presence of cerebral metastases [n, %] | 27 (30%) | |
Presence of hepatic metastases [n, %] | 29 (32%) | |
Targeted therapy [n, %] | Dabrafenib | 4 (4%) |
Dabrafenib + trametinib | 43 (47%) | |
Vemurafenib | 1 (1%) | |
Vemurafenib + cobimetinib | 36 (40%) | |
Encorafenib | 1 (1%) | |
Encorafenib + binimetinib | 6 (7%) | |
Lesion counts [n] | All | 4727 |
Lung | 2006 | |
Liver | 753 | |
Soft tissue/skin | 1155 | |
Lymph nodes | 379 | |
Skeletal | 123 | |
Spleen | 99 | |
Other | 212 | |
Patient outcome | ||
Best response (RECIST 1.1) [n, %] | CR | 11 (12%) |
PR | 43 (47%) | |
SD | 15 (16%) | |
PD | 19 (21%) | |
n/a | 3 (3%) | |
Progression-free survival for 6 months [n, %] | Yes | 36 (40%) |
No | 44 (48%) | |
n/a | 11 (12%) | |
Overall survival after 6 months [n, %] | Yes | 69 (76%) |
No | 17 (19%) | |
n/a | 5 (5%) | |
Overall survival after 12 months [n, %] | Yes | 37 (41%) |
No | 34 (37%) | |
n/a | 20 (22%) | |
Overall survival (months) [median, (95%CI)] | 26.2 (20.8–31.6) |
Binary Endpoint | ||||
---|---|---|---|---|
Best Response | PFS for 6 Months | OS at 6 Months | OS at 12 Months | |
n cases, (class 0, class 1) | 88 (34, 54) | 80 (44, 36) | 86 (17, 69) | 71 (34, 37) |
Baseline model (clinical features), [AUC (95% CI)] | 0.460 (0.362, 0.559) | 0.586 (0.479, 0.693) | 0.742 (0.631, 0.853) | 0.600 (0.489, 0.709) |
Extended model (clinical and radiomic features), [AUC (95% CI)] | 0.524 (0.431, 0.615) | 0.679 (0.579, 0.774) | 0.648 (0.524, 0.769) | 0.522 (0.424, 0.625) |
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Share and Cite
Peisen, F.; Gerken, A.; Hering, A.; Dahm, I.; Nikolaou, K.; Gatidis, S.; Eigentler, T.K.; Amaral, T.; Moltz, J.H.; Othman, A.E. Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study. Diagnostics 2023, 13, 3210. https://doi.org/10.3390/diagnostics13203210
Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE. Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study. Diagnostics. 2023; 13(20):3210. https://doi.org/10.3390/diagnostics13203210
Chicago/Turabian StylePeisen, Felix, Annika Gerken, Alessa Hering, Isabel Dahm, Konstantin Nikolaou, Sergios Gatidis, Thomas K. Eigentler, Teresa Amaral, Jan H. Moltz, and Ahmed E. Othman. 2023. "Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study" Diagnostics 13, no. 20: 3210. https://doi.org/10.3390/diagnostics13203210