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Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid...
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... inducing an "off" signal. To date, three main types of ICIs, including programmed cell death protein 1 (PD-1), its ligand (PD-L1), and cytotoxic T lymphocyte-associated antigen 4, have been developed for clinical use in treating various cancers [5], as summarized in Table 1 [9]. ICIs have been used to treat lung cancer, melanoma, brain metastases, head and neck squamous cell carcinoma, renal cell carcinoma, bladder cancer, endometrial cancer, cervical cancer, and ovarian cancer [10][11][12]. ...Similar publications
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases....
Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also be...
Background and objectiveFor patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to esta...
The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelli...
Citations
... Moreover, modern oncologic imaging often employs a multimodal approach, combining conventional radiologic imaging with nuclear medicine techniques. To accommodate these evolving practices, KJR has recently New Oncologic Imaging Section in the Korean Journal of Radiology Seong Ho Park, Editor-in-Chief Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea published a few articles in a distinct subject category of Oncologic Imaging, even though a formal section dedicated to this topic has not yet been established [2]. ...
... Integrating AI into imaging allows clinicians to find a segment of interest and then extract features to draw correlations and identify the most important features. AI models are created with these data and can be used to diagnose, prognosticate, treat, and monitor metastatic melanoma [2,4,11,13,14]. ...
... Traditional cancer treatments focus on targeting cell division [14]. Their effectiveness can be assessed based on the regression and shrinkage of tumors, which can best be measured by CT using Response Evaluation Criteria in Solid Tumors (RECIST) [33]. ...
... However, immunotherapy causes different patterns of tumor progression, resulting in a need for fundamental changes in the use of imaging modalities to study their beneficial and adverse effects [14]. First, pseudotumor progression is a pattern that results in a transient increase in tumor size [34]. ...
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
... Recently, considering tumor heterogeneity and differences in therapy response, artificial intelligence (AI) approaches, radiomics and machine learning algorithms, have emerged as non-invasive technologies using medical imaging analyses. AI can extract significant quantitative data from patients' medical images and correlate image features with diagnostic and therapeutic outcomes [52,53]. Radiomics has been applied in lymphomas to examine baseline FDG-PET for differential diagnosis from other malignancies and in evaluations of bone marrow involvement and pre-treatment risk [54,55], but, currently, no data are available for radiomic analyses in the context of assessments of the response to immunotherapy in lymphomas [56,57]. ...
Immune checkpoint inhibitors are currently the standard of care for many advanced solid tumors, and they have been recently approved for the treatment of relapsed/refractory Hodgkin lymphoma and primary mediastinal B cell lymphoma. Assessments of the response to immunotherapy may be complicated by the occurrence of the flare/pseudoprogression phenomenon, consisting of initial tumor enlargement and even the appearance of new lesions, followed by a response, which may initially be indistinguishable from true progression. There have been efforts to characterize and capture the new patterns of response observed during immunotherapy, namely, pseudoprogression and delayed response, and several immune-related response criteria have been proposed. Confirming progression on a subsequent scan and measuring the total tumor burden are both common in immune-related criteria. Due to the peculiarity of hematologic malignancies, lymphoma-specific immune-related criteria have been developed (LYRIC), and they have been evaluated in research studies in comparison to the Lugano Classification. In this review work, we illustrate the evolution of the response criteria in lymphomas from the first CT-based criteria to the development of the PET-based Lugano Classification, further refined to take into account the flare phenomenon encountered during immunotherapy. We also describe the additional contribution of PET-derived volumetric parameters to the interpretation of responses during immunotherapy.
... Pseudoprogression, presenting as increasing tumor size or new lesions, is attributed to T-cell infiltration or local tissue reactions but not tumor cell progression [199] and is challenging to distinguish on medical images alone. There have been reports that AI can support response assessment by accurately analyzing pseudoprogression [200,201]. A prior study included thirty-four glioblastoma patients (94% isocitrate dehydrogenase-wildtype glioblastoma) who underwent static and dynamic FET PET scans. ...
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
... In contrast to conventional treatments, immunotherapy involves a complex process that includes different phases and during each one, the immune system is activated. Such as, a number of immune cells move to the target with increasing in target volume and/or new lesion growth [139,149,150]. This process can cause an unusual response pattern known Diagnostics 2023, 13, 302 3 of 20 as pseudoprogression [139]. ...
... In contrast to conventional treatments, immunotherapy involves a complex process that includes different phases and during each one, the immune system is activated. Such as, a number of immune cells move to the target with increasing in target volume and/or new lesion growth [139,149,150]. This process can cause an unusual response pattern known as pseudoprogression [139]. ...
Immunotherapy denotes an exemplar change in an oncological setting. Despite the effective application of these treatments across a broad range of tumors, only a minority of patients have beneficial effects. The efficacy of immunotherapy is affected by several factors, including human immunity, which is strongly correlated to genetic features, such as intra-tumor heterogeneity. Classic imaging assessment, based on computed tomography (CT) or magnetic resonance imaging (MRI), which is useful for conventional treatments, has a limited role in immunotherapy. The reason is due to different patterns of response and/or progression during this kind of treatment which differs from those seen during other treatments, such as the possibility to assess the wide spectrum of immunotherapy-correlated toxic effects (ir-AEs) as soon as possible. In addition, considering the unusual response patterns, the limits of conventional response criteria and the necessity of using related immune-response criteria are clear. Radiomics analysis is a recent field of great interest in a radiological setting and recently it has grown the idea that we could identify patients who will be fit for this treatment or who will develop ir-AEs.
... One study used deep learning artificial intelligence-based algorithms to analyze the CheckMate-038 trial and found that it could predict the expression of tumor-specific T-cell receptors in melanoma patients receiving immunotherapy (118). Most of these new technologies are in the exploratory stage and may be used to test drug efficacy, predict adverse effects (119)(120)(121), screen for potential biomarkers, and aid in drug development. ...
Immunotherapy has shown great promise in the field of oncology, and recent clinical trials have illustrated that immune checkpoint blockade (ICB) is safe and effective at treating a range of tumor types. Cervical cancer (CC) is the fourth most common malignancy in women. However, first-line treatments for locally advanced cervical cancer (LACC) and recurrent/metastatic (R/M) CC have limited efficacy. Thus, it is necessary to explore new treatment approaches. The National Comprehensive Cancer Network (NCCN) currently recommends pembrolizumab, a programmed cell death protein 1 (PD-1) monoclonal antibody, as a first line therapy for individuals with R/M CC. This study reviews the progress of ICB therapy for LACC and R/M CC and describes the current status of the combination of ICB therapy and other therapeutic modalities, including radiotherapy, chemotherapy, targeted therapy, and other immunotherapies. The focus is placed on studies published since 2018 with the aim of highlighting novel CC-specific immunotherapeutic approaches and treatment targets.
Chimeric antigen receptor (CAR)-T cell-based immunotherapy has emerged as a path-breaking strategy for certain hematological malignancies. Assessment of the response to CAR-T therapy using quantitative imaging techniques such as positron emission tomography/computed tomography (PET/CT) has been broadly investigated. However, the definitive role of PET/CT in CAR-T therapy remains to be established. [ ¹⁸ F]FDG PET/CT has demonstrated high sensitivity and specificity for differentiating patients with a partial and complete response after CAR-T therapy in lymphoma. The early therapeutic response and immune-related adverse effects such as cytokine release syndrome and immune effector cell–associated neurotoxicity syndrome can also be detected on [ ¹⁸ F]FDG PET images. In otherwise asymptomatic lymphoma patients with partial response following CAR-T therapy, the only positive findings could be abnormal PET/CT results. In multiple myeloma, a negative [ ¹⁸ F]FDG PET/CT after receiving B-cell maturation antigen-directed CAR-T therapy has been associated with a favorable prognosis. In leukemia, [ ¹⁸ F]FDG PET/CT can detect extramedullary metastases and treatment responses after therapy. Hence, PET/CT is a valuable imaging tool for patients undergoing CAR-T therapy for pretreatment evaluation, monitoring treatment response, assessing safety, and guiding therapeutic strategies. Developing guidelines with standardized cutoff values for various PET parameters and tumor cell–specific tracers may improve the efficacy and safety of CAR-T therapy.
Objective
The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.Methods
The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.ResultsThe best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.Conclusions
The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
