Early Tumor Size Reduction of at least 10% at the First Follow-Up Computed Tomography Can Predict Survival in the Setting of Advanced Melanoma and Immunotherapy

Almansour, Haidara; Afat, Saif; Serna-­Higuita, Lina María; Amaral, Teresa; Schraag, Amadeus; Peisen, Felix; Brendlin, Andreas; Seith, Ferdinand; Klumpp, Bernhard; Eigentler, Thomas; Othman, Ahmed E.

doi:10.1016/j.acra.2021.04.015

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…The year of publication of the 36 included studies was primarily between 2019 and 2022. Of the 36 studies, 18 were conducted in China ( 16 – 33 ) and 5 were conducted in Germany ( 34 – 38 ). The primary outcome measures were treatment response, OS, and PFS.…”

Section: Resultsmentioning

confidence: 99%

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Li,

Dan,

2024

Front. Immunol.

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BackgroundThe emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.MethodsRelevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.ResultsA total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629–0.828) for PFS in the training set, 0.760 (95%CI: 0.728–0.792) and 0.819 (95%CI: 0.757–0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721–0.771) and 0.700 (95%CI: 0.677–0.724) for OS in the training and validation sets, respectively.ConclusionMachine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

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Section: Resultsmentioning

confidence: 99%

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Li,

Dan,

2024

Front. Immunol.

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“…Results from a meta-analysis that included patients from 10 different trials indicated that ETS showed enormous potential for supporting the identification of patients that would be sensitive to treatment and patients that would benefit from treatment continuation [13]. Based on those promising results, ETS was recently identified as an imaging biomarker for various other cancer entities, and it specifically showed potential as a predictive factor in patients treated with immunotherapy [14][15][16][17][18][19].…”

Section: Discussionmentioning

confidence: 99%

“…Originally identified as an imaging biomarker for patients with colorectal cancer undergoing chemotherapy, ETS was also shown to be predictive of outcomes in patients with other cancer entities [13][14][15][16]. Several recent studies identified ETS as a highly promising imaging biomarker, specifically for outcomes in patients treated with immunotherapy [17][18][19]. Moreover, ETS was identified as a superior parameter for assessing the treatment response, compared to the conventional response evaluation criteria in solid tumors (RECIST) and the modified RECIST (mRECIST) criteria.…”

Section: Introductionmentioning

confidence: 99%

The prognostic role of early tumor shrinkage in patients with hepatocellular carcinoma undergoing immunotherapy

et al. 2022

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Background Early tumor shrinkage (ETS) has been identified as a promising imaging biomarker for patients undergoing immunotherapy for several cancer entities. This study aimed to validate the potential of ETS as an imaging biomarker for patients undergoing immunotherapy for hepatocellular carcinoma (HCC). Methods We screened all patients with HCC that received immunotherapy as the first or subsequent line of treatment at our tertiary care center between 2016 and 2021. ETS was defined as the reduction in the sum of the sizes of target lesions, between the initial imaging and the first follow-up. The ETS was compared to the radiologic response, according to the modified response evaluation criteria in solid tumors (mRECIST). Furthermore, we evaluated the influence of ETS on overall survival (OS), progression-free survival (PFS), and the alpha-fetoprotein (AFP) response. Results The final analysis included 39 patients with available cross-sectional imaging acquired at the initiation of immunotherapy (baseline) and after 8–14 weeks. The median ETS was 5.4%. ETS was significantly correlated with the response according to mRECIST and with the AFP response. Patients with an ETS ≥10% had significantly longer survival times after the first follow-up, compared to patients with < 10% ETS (15.1 months vs. 4.0 months, p = 0.008). Additionally, patients with both an ETS ≥10% and disease control, according to mRECIST, also had significantly prolonged PFS times after the initial follow-up (23.6 months vs. 2.4 months, p < 0.001). Conclusion ETS was strongly associated with survival outcomes in patients with HCC undergoing immunotherapy. Thus, ETS is a readily assessable imaging biomarker that showed potential for facilitating a timely identification of patients with HCC that might benefit from immunotherapy.

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“…106 Several aspects have matured, such as establishing non-invasive biomarkers using imaging, identifying irAEs through clinical manifestations and imaging diagnostic techniques, assessing the efficacy of immunotherapy based on solid tumor assessment criteria, and using imaging radiomics combined with machine learning models for predicting the efficacy of immunotherapy in NSCLC. 83,102,107 Given the rapid development of immunotherapy, our analysis, along with keyword clustering and emerging trends, indicates that future attention may gradually shift from lung cancer to other types of cancer such as liver cancer, breast cancer, etc. These tumors have historically been defined as immunologically "cold" tumors.…”

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confidence: 99%

Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis

Tang,

Fan,

Wang

et al. 2024

JMDH

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With the rapid development of immunotherapy, cancer treatment has entered a new phase. Medical imaging, as a primary diagnostic method, is closely related to cancer immunotherapy. However, until now, there has been no systematic bibliometric analysis of the state of this field. Therefore, the main purpose of this article is to clarify the past research trajectory, summarize current research hotspots, reveal dynamic scientific developments, and explore future research directions. Patients and Methods: A comprehensive search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to immunotherapy specifically for the medical imaging of carcinoma. The search spanned the period from the year 2003 to 2023. Several analytical tools were employed. These included CiteSpace (6.2.4), and the Microsoft Office Excel (2016). Results: By searching the database, a total of 704 English articles published between 2003 and 2023 were obtained. We have observed a rapid increase in the number of publications since 2018. The two most active countries are the United States (n=265) and China (n=170). Pittock, Sean J and Abu-sbeih, Hamzah are very concerned about the relationship between cancer immunotherapy and medical images and have published more academic papers (n = 5; n = 4). Among the top 10 co-cited authors, Topalian Sl (n=43) cited ranked first, followed by Graus F (n=40) cited. According to clustering, timeline, and burst word analysis, the results show that the current research focus is on "MRI", "deep learning", "tumor microenvironment" and so on. Conclusion: Medical imaging and cancer immunotherapy are hot topics. The United States is the country with the most publications and the greatest influence in this field, followed by China. "MRI", "PET/PET-CT", "deep learning", "immune-related adverse events" and "tumor microenvironment" are currently hot research topics and potential targets.

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Early Tumor Size Reduction of at least 10% at the First Follow-Up Computed Tomography Can Predict Survival in the Setting of Advanced Melanoma and Immunotherapy

Cited by 5 publications

References 33 publications

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

The prognostic role of early tumor shrinkage in patients with hepatocellular carcinoma undergoing immunotherapy

Cancer Immunotherapy and Medical Imaging Research Trends from 2003 to 2023: A Bibliometric Analysis

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