Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy

Peisen, Felix; Hänsch, Annika; Hering, Alessa; Brendlin, Andreas; Afat, Saif; Nikolaou, Konstantin; Gatidis, Sergios; Eigentler, Thomas; Amaral, Teresa; Moltz, Jan Hendrik; Othman, Ahmed E.

doi:10.3390/cancers14122992

Cited by 17 publications

(12 citation statements)

References 40 publications

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“…Three previous smaller studies have investigated radiomics for this purpose, with conflicting findings. The studies by Trebeschi et al [22] and Peisen et al [23] report a significant discriminative value of radiomics for treatment outcomes (AUROC=0.78 on a dataset of 80 patients, and AUROC=0.64 on a dataset of 262 patients, respectively). In contrast, Brendlin et al [24] reported a non-discriminative performance, despite using a very similar methodology (AUROC=0.50 in 140 patients).…”

Section: Radiomics Are By Now An Established Modality For Diagnosis P...mentioning

confidence: 99%

CT radiomics to predict checkpoint inhibitors treatment outcomes in patients with advanced cutaneous melanoma

Maat

Duin

Elias³

et al. 2022

Preprint

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Introduction Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumor characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting durable clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. Methods Patients who received first-line anti-PD1 with or without anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict durable clinical benefit, defined as stable disease for more than six months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-center-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model. Results A total of 620 patients were included, of which 59.2% experienced durable clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95%CI 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95%CI 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95%CI 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). Discussion The radiomics model achieved a moderate predictive value of durable clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT derived radiomics and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.

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Section: Radiomics Are By Now An Established Modality For Diagnosis P...mentioning

confidence: 99%

CT radiomics to predict checkpoint inhibitors treatment outcomes in patients with advanced cutaneous melanoma

Maat

Duin

Elias³

et al. 2022

Preprint

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“…A recent study in patients with advanced NSCLC who were treated with ICI presented an approach that evaluates dynamic changes in specific tumor radiophenotypic attributes between baseline and post-treatment CT scans [40]. In addition, a few studies have extracted imaging biomarkers to assess the correlation between pretreatment CT texture parameters and survival prediction in patients with metastatic melanoma who received anti-PD-1 monoclonal antibodies [41][42][43][44]. Study findings by Trebeschi et al on a small melanoma cohort proved an association between tumor textural radiomic patterns and responses to immunotherapy [45].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Tabari

Cox

D'Amore

et al. 2023

Cancers

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Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

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“…Radiomic features are highly dependent on image acquisition (machine variability) and reconstruction (software variability) [20]. Current studies show that while there are statistically significant radiomic features associated with overall survival and progression free survival, these features are based on small sample sizes and it would be difficult to have them universally accepted [23,24,55].…”

Section: Methodsmentioning

confidence: 99%

“…These methods could assist with a more precise melanoma diagnosis and individualised therapy. They can be used to predict immunotherapy response in metastatic melanoma prior to commencing treatment [20,22,[24][25][26]. Furthermore, radiomics can aide in-vivo classification of disease, reducing invasive diagnostic testing such as biopsy [21].…”

Section: Current Utilitymentioning

confidence: 99%

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The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective

Yee,

Rosendahl,

Aoude

2023

Melanoma Research

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Clinical dermatoscopy and pathological slide assessment are essential in the diagnosis and management of patients with cutaneous melanoma. For those presenting with stage IIC disease and beyond, radiological investigations are often considered. The dermatoscopic, whole slide and radiological images used during clinical care are often stored digitally, enabling artificial intelligence (AI) and convolutional neural networks (CNN) to learn, analyse and contribute to the clinical decision-making. To review the literature on the progression, capabilities and limitations of AI and CNN and its use in diagnosis and management of cutaneous melanoma. A keyword search of the Medline database for articles relating to cutaneous melanoma. Full-text articles were reviewed if they related to dermatoscopy, pathological slide assessment or radiology. Through analysis of 95 studies, we demonstrate that diagnostic accuracy of AI/CNN can be superior (or at least equal) to clinicians. However, variability in image acquisition, pre-processing, segmentation, and feature extraction remains challenging. With current technological abilities, AI/CNN and clinicians synergistically working together are better than one another in all subspecialty domains relating to cutaneous melanoma. AI has the potential to enhance the diagnostic capabilities of junior dermatology trainees, primary care skin cancer clinicians and general practitioners. For experienced clinicians, AI provides a cost-efficient second opinion. From a pathological and radiological perspective, CNN has the potential to improve workflow efficiency, allowing clinicians to achieve more in a finite amount of time. Until the challenges of AI/CNN are reliably met, however, they can only remain an adjunct to clinical decision-making.

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Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy

Cited by 17 publications

References 40 publications

CT radiomics to predict checkpoint inhibitors treatment outcomes in patients with advanced cutaneous melanoma

CT radiomics to predict checkpoint inhibitors treatment outcomes in patients with advanced cutaneous melanoma

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective

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