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Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.

ABSTRACT: Advanced malignant melanoma represents a public health matter due to its rising incidence and aggressiveness. Novel therapies such as immunotherapy are showing promising results with improved progression free and overall survival in melanoma patients. However, novel targeted and immunotherapies could generate atypical patterns of response which are nowadays a big challenge since imaging criteria (ie Recist 1.1) have not been proven to be always reliable to assess response. Radiomics and in particular texture analysis (TA) represent new quantitative methodologies which could reduce the impact of these limitations providing most robust data in support of clinical decision process. The aim of this paper was to review the state of the art of radiomics/TA when it is applied to the imaging of metastatic melanoma patients.

SUBMITTER: Guerrisi A

PROVIDER: S-EPMC7050080 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.

Guerrisi Antonino A Loi Emiliano E Ungania Sara S Russillo Michelangelo M Bruzzaniti Vicente V Elia Fulvia F Desiderio Flora F Marconi Raffaella R Solivetti Francesco Maria FM Strigari Lidia L

Cancer medicine 20200117 5

Advanced malignant melanoma represents a public health matter due to its rising incidence and aggressiveness. Novel therapies such as immunotherapy are showing promising results with improved progression free and overall survival in melanoma patients. However, novel targeted and immunotherapies could generate atypical patterns of response which are nowadays a big challenge since imaging criteria (ie Recist 1.1) have not been proven to be always reliable to assess response. Radiomics and in parti ...[more]

PMID: 31951322

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Project description:Background Race influences medical decision making, but its impact on advanced heart failure therapy allocation is unknown. We sought to determine whether patient race influences allocation of advanced heart failure therapies. Methods and Results Members of a national heart failure organization were randomized to clinical vignettes that varied by patient race (black or white man) and were blinded to study objectives. Participants (N=422) completed Likert scale surveys rating factors for advanced therapy allocation and think-aloud interviews (n=44). Survey results were analyzed by least absolute shrinkage and selection operator and multivariable regression to identify factors influencing advanced therapy allocation, including interactions with vignette race and participant demographics. Interviews were analyzed using grounded theory. Surveys revealed no differences in overall racial ratings for advanced therapies. Least absolute shrinkage and selection operator regression selected no interactions between vignette race and clinical factors as important in allocation. However, interactions between participants aged ≥40 years and black vignette negatively influenced heart transplant allocation modestly (-0.58; 95% CI, -1.15 to -0.0002), with adherence and social history the most influential factors. Interviews revealed sequential decision making: forming overall impression, identifying urgency, evaluating prior care appropriateness, anticipating challenges, and evaluating trust while making recommendations. Race influenced each step: avoiding discussing race, believing photographs may contribute to racial bias, believing the black man was sicker compared with the white man, developing greater concern for trust and adherence with the black man, and ultimately offering the white man transplantation and the black man ventricular assist device implantation. Conclusions Black race modestly influenced decision making for heart transplant, particularly during conversations. Because advanced therapy selection meetings are conversations rather than surveys, allocation may be vulnerable to racial bias.

Project description:PurposeTo evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients.MethodsPatients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1-3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student's t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS).ResultsAll radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS.ConclusionAll single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

Project description:ObjectivesTo determine the diagnostic ability of cervical mucosa radiomics signature of sagittal T2WI and T1 contrast-enhanced (CE) imaging in detecting early-stage cervical cancers with negative MRI.MethodsPreoperative images of postoperative pathology confirmed early-stage cervical cancer patients and normal cervix patients admitted to our hospital between January 2013 and December 2020 were retrospectively reviewed. Patients with cancer signals on T2WI, T1CE and DWI were deleted. Regions of interests (ROIs) were delineated on cervical mucosa (from cervical canal to cervical dome) with 5 mm width on sagittal T2WI and T1CE. The maximum-relevance and minimumredundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the calculation of radiomics signature scores. Diagnostic performance was assessed and compared between radiomics prediction models (model 1: T1CE; model 2: T2WI; model 3: model one combined with model 2). Differential diagnostic ability of radiomics signature in detecting lymphatic vascular space invasion (LVSI) was further explored.ResultsDiagnostic performance of model three was higher than model 1 and model 2 both in primary (model 3 0.874, model 1 0.857, model 2 0.816) and validation (model 3 0.853, model 1 0.847, model 2 0.634) cohorts. Model 3 showed statistical diagnostic difference compared with model 2 (primary p = 0.008, validation p = 0.000). However, the diagnostic improvement ability of model 3 showed no statistical difference compared with model 1 (primary p = 0.351, validation p = 0.739). Diagnostic efficiency of model 3 in detecting LVSI was not apparent (AUC 0.64).ConclusionsRadiomics analysis of cervical mucosa combining T1CE and T2WI is promising for predicting MRI invisible early-stage cervical cancers, however further ability in detecting LVSI was not apparent.Advances in knowledgeConventional MRI was originally defined as meaningless in very early-stage cervical cancers. However, whether MRI radiomics analysis of cervical mucosa can detecting tiny changes of invisible early stage cervical cancers has not been researched yet.

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Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.

Publications

Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.

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