Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

Maat, Laurens S. ter; Duin, Isabella A.J. van; Elias, Sjoerd G.; Diest, Paul J. van; Pluim, Josien P. W.; Verhoeff, Joost J.C.; Jong, Pim A. de; Leiner, Tim; Veta, Mitko; Suijkerbuijk, Karijn

doi:10.1016/j.ejca.2022.07.034

Cited by 14 publications

(14 citation statements)

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“…The strengths of this work are the large sample size, the multicenter design and extensive hyperparameter optimization. This is the largest work published on radiomics for prediction of checkpoint inhibitor treatment outcomes in any malignancy [21]. This large size adds to the weight of the presented conclusion.…”

Section: Strengths and Limitationsmentioning

confidence: 80%

“…This makes radiomics a cheap and non-invasive modality to, for example, discern benign from malignant lung nodules [18], estimate prognosis in non-small cell lung cancer (NSCLC) patients [19] and assess mutation status in glioblastoma [20]. Regarding prediction of checkpoint inhibitor treatment outcomes, promising findings have been published, particularly in NSCLC patients [21].…”

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

confidence: 99%

“…Studies on radiomics should assess the added value over simpler predictors. Many smaller exploratory studies have been conducted into the predictive value of radiomics for checkpoint inhibitor outcomes across different malignancies [21]. Their findings are almost exclusively positive, but the added value over clinical predictors was seldomly assessed.…”

Section: Future Researchmentioning

confidence: 99%

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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: Strengths and Limitationsmentioning

confidence: 80%

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

confidence: 99%

Section: Future Researchmentioning

confidence: 99%

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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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“…For example, sex, World Health Organisation (WHO) performance score, serum lactate dehydrogenase and stage of disease have been shown to be associated with response. [6][7][8] Furthermore, the presence of symptomatic brain metastases is associated with a low response rate. 9,10 Unfortunately, no single biomarker is currently available that can be confidently relied upon to predict ICI outcome for the majority of advanced melanoma patients.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous clinical prognostic factors have been examined. For example, sex, World Health Organisation (WHO) performance score, serum lactate dehydrogenase and stage of disease have been shown to be associated with response 6–8 . Furthermore, the presence of symptomatic brain metastases is associated with a low response rate 9,10 .…”

Section: Introductionmentioning

confidence: 99%

A prediction model for response to immune checkpoint inhibition in advanced melanoma

van Duin,

Verheijden,

van Diest

et al. 2024

Intl Journal of Cancer

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Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population‐based cohort of 3525 patients with advanced cutaneous melanoma treated with anti‐PD‐1‐based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal–external cross‐validation. Included patients received anti‐PD‐1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence—all at start of ICI—, and location and type of primary melanoma, the presence of satellites and/or in‐transit metastases at primary diagnosis and sex. The over‐optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64–0.66). The range of predicted response probabilities was 7%–81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression‐free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis.

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Clinical trials of neoadjuvant immune checkpoint inhibitors for early-stage operable colon and rectal cancer

Veen,

Kanani,

Lea

et al. 2023

Cancer Immunol Immunother

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Background Immune checkpoint inhibitors (ICI) have become first-line treatment for metastatic colorectal cancer (CRC) with deficient mismatch repair (dMMR). Despite the remarkable response reported in preliminary trials, the role of ICI in patients with early-stage, operable CRC remains unclear. The aim of this study was to investigate trials on neoadjuvant ICI in operable CRC. Materials and methods Scoping review of clinical trial registries (Clinicaltrials.gov and EU clinical trial registers) and PubMed/Medline database of trials on neoadjuvant ICI for operable CRC was done up to December 2022. Results Some 40 trials investigating neoadjuvant ICI for early-stage, operable CRC were identified, including five published trials and three conference abstracts. Preclinical phase I/II trial predominated with only three clinical phase III trials. Few trials investigated neoadjuvant ICI as the only intervention (monotherapy). Trials in rectal cancer were designed for combined ICI with chemo(radio)therapy, only 8 trials stating an MSI/dMMR status for inclusion, one designed for MSS/pMMR only and, the rest agnostic for MMR status. Thirty-eight (95%) trials investigated programmed cell death protein 1 (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors. PD-1/PD-L1 inhibitors were combined with vascular endothelial growth factor (VEGF) inhibitor or with cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) inhibitor, in two trials each, respectively. Pathological complete response as primary outcome after surgery was the most frequently used study endpoint. In rectal cancer, six trials included a “watch and wait” strategy for patients with complete clinical response. No “watch and wait” study design for colon cancer after neoadjuvant ICI were identified. Conclusion High response rates from neoadjuvant ICI in early-stage colon and rectal cancer are reported in phase I/II studies. Contemporary trial designs are heterogeneous, with few comparable inclusion criteria, use of several drug combinations and durations and, wide variation of endpoints reported. Harmonizing clinical and translational aspects including survival data is needed for improved future trial designs with clinical impact.

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Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

Cited by 14 publications

References 152 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

A prediction model for response to immune checkpoint inhibition in advanced melanoma

Clinical trials of neoadjuvant immune checkpoint inhibitors for early-stage operable colon and rectal cancer

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