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Geady C, Abbas-Aghababazadeh F, Kohan A, Schuetze S, Shultz D, Haibe-Kains B. Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease. Comput Med Imaging Graph 2024; 116:102413. [PMID: 38945043 DOI: 10.1016/j.compmedimag.2024.102413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 04/08/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.
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Affiliation(s)
- Caryn Geady
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada
| | | | - Andres Kohan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Scott Schuetze
- Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - David Shultz
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada; Department of Medicine, University of Michigan, Ann Arbor, MI, USA; Vector Institute for Artificial Intelligence, Toronto, Canada; Ontario Institute for Cancer Research, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada; Medical Biophysics, University of Toronto, Toronto, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada; Ontario Institute for Cancer Research, Toronto, Canada; Department of Computer Science, University of Toronto, Toronto, Canada; Department of Biostatistics, Dalla Lana School of Public Health, Toronto, Canada.
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Geady C, Abbas-Aghababazadeh F, Kohan A, Schuetze S, Shultz D, Haibe-Kains B. Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23294942. [PMID: 37873411 PMCID: PMC10593058 DOI: 10.1101/2023.09.22.23294942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy. Highlights Intensity values in CT scans and their corresponding spatial distribution convey important information.A model to predict lesion-specific response to systemic treatment using image-derived features is proposed.Up to a 4.5-fold increase in predictive capacity compared to a no-skill classifier was obtained, with AUPRC of 0.70 for the most precise model (FDR = 0.05).Assessing treatment response on a lesion-level acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.
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Wu A, Hu T, Lai C, Zeng Q, Luo L, Shu X, Huang P, Wang Z, Feng Z, Zhu Y, Cao Y, Li Z. Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations. Cancer Med 2024; 13:e70153. [PMID: 39206620 PMCID: PMC11358765 DOI: 10.1002/cam4.70153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Revised: 08/06/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. METHODS Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. RESULTS We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. CONCLUSIONS The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.
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Affiliation(s)
- Ahao Wu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
- Medical Innovation CentreThe First Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
| | - Tengcheng Hu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Chao Lai
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Qingwen Zeng
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Lianghua Luo
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Xufeng Shu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Pan Huang
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zhonghao Wang
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zongfeng Feng
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Yanyan Zhu
- Department of RadiologyThe First Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
| | - Yi Cao
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zhengrong Li
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
- Department of Digestive Surgery, Digestive Disease HospitalThe Third Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
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Uher O, Hadrava Vanova K, Taïeb D, Calsina B, Robledo M, Clifton-Bligh R, Pacak K. The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives. Endocr Rev 2024; 45:521-552. [PMID: 38377172 PMCID: PMC11244254 DOI: 10.1210/endrev/bnae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024]
Abstract
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.
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Affiliation(s)
- Ondrej Uher
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - Katerina Hadrava Vanova
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - David Taïeb
- Department of Nuclear Medicine, CHU de La Timone, Marseille 13005, France
| | - Bruna Calsina
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Familiar Cancer Clinical Unit, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institute of Health Carlos III (ISCIII), Madrid 28029, Spain
| | - Roderick Clifton-Bligh
- Department of Endocrinology, Royal North Shore Hospital, Sydney 2065, NSW, Australia
- Cancer Genetics Laboratory, Kolling Institute, University of Sydney, Sydney 2065, NSW, Australia
| | - Karel Pacak
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
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Geady C, Patel H, Peoples J, Simpson A, Haibe-Kains B. Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.04.24309964. [PMID: 39006417 PMCID: PMC11245050 DOI: 10.1101/2024.07.04.24309964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Background Radiomics traditionally focuses on analyzing a single lesion within a patient to extract tumor characteristics, yet this process may overlook inter-lesion heterogeneity, particularly in the multi-metastatic setting. There is currently no established method for combining radiomic features in such settings, leading to diverse approaches with varying strengths and limitations. Our quantitative review aims to illuminate these methodologies, assess their replicability, and guide future research toward establishing best practices, offering insights into the challenges of multi-lesion radiomic analysis across diverse datasets. Methods We conducted a comprehensive literature search to identify methods for integrating data from multiple lesions in radiomic analyses. We replicated these methods using either the author's code or by reconstructing them based on the information provided in the papers. Subsequently, we applied these identified methods to three distinct datasets, each depicting a different metastatic scenario. Results We compared ten mathematical methods for combining radiomic features across three distinct datasets, encompassing a total of 16,850 lesions in 3,930 patients. Performance of these methods was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. We observed variable performance in methods across datasets. However, no single method consistently outperformed others across all datasets. Notably, while some methods surpassed total tumor volume analysis in certain datasets, others did not. Averaging methods showed higher median performance in patients with colorectal liver metastases, and in soft tissue sarcoma, concatenation of radiomic features from different lesions exhibited the highest median performance among tested methods. Conclusions Radiomic features can be effectively selected or combined to estimate patient-level outcomes in multi-metastatic patients, though the approach varies by metastatic setting. Our study fills a critical gap in radiomics research by examining the challenges of radiomic-based analysis in this setting. Through a comprehensive review and rigorous testing of different methods across diverse datasets representing unique metastatic scenarios, we provide valuable insights into effective radiomic analysis strategies.
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Affiliation(s)
- Caryn Geady
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Hemangini Patel
- Biomedical Computing, Queen's University, Kingston, Ontario, Canada
| | - Jacob Peoples
- School of Computing, Queen's University, Kingston, Ontario, Canada
| | - Amber Simpson
- School of Computing, Queen's University, Kingston, Ontario, Canada
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
| | - Benjamin Haibe-Kains
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
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Mangla A, Lee C, Mirsky MM, Wang M, Rothermel LD, Hoehn R, Bordeaux JS, Carrol BT, Theuner J, Li S, Fu P, Kirkwood JM. Neoadjuvant Dual Checkpoint Inhibitors vs Anti-PD1 Therapy in High-Risk Resectable Melanoma: A Pooled Analysis. JAMA Oncol 2024; 10:612-620. [PMID: 38546551 PMCID: PMC10979364 DOI: 10.1001/jamaoncol.2023.7333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/07/2023] [Indexed: 04/01/2024]
Abstract
Importance Despite the clear potential benefits of neoadjuvant therapy, the optimal neoadjuvant regimen for patients with high-risk resectable melanoma (HRRM) is not known. Objective To compare the safety and efficacy of dual checkpoint inhibitors with anti-programmed cell death protein-1 (anti-PD1) therapy in a neoadjuvant setting among patients with HRRM. Design, Setting, and Participants In this pooled analysis of clinical trials, studies were selected provided they investigated immune checkpoint inhibitor treatment, were published between January 2018 and March 2023, and were phase 1, 2, or 3 clinical trials. Participant data included in the analysis were derived from trials evaluating the efficacy and safety of anti-PD1 monotherapy and the combination of anti-cytotoxic T lymphocyte-associated protein-4 with anti-PD1 in the neoadjuvant setting, specifically among patients with HRRM. Interventions Patients were treated with either anti-PD1 monotherapy; dual checkpoint inhibition (DCPI) with a conventional dose of 3-mg/kg ipilimumab and 1-mg/kg nivolumab; or DCPI with an alternative-dose regimen of 1-mg/kg ipilimumab and 3-mg/kg nivolumab. Main Outcomes and Measures The main outcomes were radiologic complete response (rCR), radiologic overall objective response (rOOR), and radiologic progressive disease. Also, pathologic complete response (pCR), the proportion of patients undergoing surgical resection, and occurrence of grade 3 or 4 immune-related adverse events (irAEs) were considered. Results Among 573 patients enrolled in 6 clinical trials, neoadjuvant therapy with DCPI was associated with higher odds of achieving pCR compared with anti-PD1 monotherapy (odds ratio [OR], 3.16; P < .001). DCPI was associated with higher odds of grade 3 or 4 irAEs compared with anti-PD1 monotherapy (OR, 3.75; P < .001). When comparing the alternative-dose ipilimumab and nivolumab (IPI-NIVO) regimen with conventional-dose IPI-NIVO, no statistically significant difference in rCR, rOOR, radiologic progressive disease, or pCR was noted. However, the conventional-dose IPI-NIVO regimen was associated with increased grade 3 or 4 irAEs (OR, 4.76; P < .001). Conventional-dose IPI-NIVO was associated with greater odds of achieving improved rOOR (OR, 1.95; P = .046) and pCR (OR, 2.99; P < .001) compared with anti-PD1 monotherapy. The alternative dose of IPI-NIVO also was associated with higher odds of achieving rCR (OR, 2.55; P = .03) and pCR (OR, 3.87; P < .001) compared with anti-PD1 monotherapy. The risk for grade 3 or 4 irAEs is higher with both the conventional-dose (OR, 9.59; P < .001) and alternative-dose IPI-NIVO regimens (OR, 2.02; P = .02) compared with anti-PD1 monotherapy. Conclusion and Relevance In this pooled analysis of 6 clinical trials, although DCPI was associated with increased likelihood of achieving pathological and radiologic responses, the associated risk for grade 3 or 4 irAEs was significantly lower with anti-PD1 monotherapy in the neoadjuvant setting for HRRM. Additionally, compared with alternative-dose IPI-NIVO, the conventional dose of IPI-NIVO was associated with increased risk for grade 3 or 4 irAEs, with no significant distinctions in radiologic or pathologic efficacy.
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Affiliation(s)
- Ankit Mangla
- Department of Hematology and Oncology, University Hospitals Seidman Cancer Center, Cleveland, Ohio
- Department of Hematology and Oncology, Case Western Reserve University School of Medicine, Cleveland, Ohio
- Case Comprehensive Cancer Center, Cleveland, Ohio
| | - Chanmi Lee
- Department of Internal Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Matthew M. Mirsky
- Department of Hematology and Oncology, University Hospitals Seidman Cancer Center, Cleveland, Ohio
- Department of Hematology and Oncology, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Margaret Wang
- Department of Internal Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Luke D. Rothermel
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Richard Hoehn
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Jeremy S. Bordeaux
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Dermatology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Bryan T. Carrol
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Dermatology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Jason Theuner
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Otolaryngology, University Hospitals Cleveland Medical Center, Cleveland Ohio
| | - Shawn Li
- Case Comprehensive Cancer Center, Cleveland, Ohio
- Department of Otolaryngology, University Hospitals Cleveland Medical Center, Cleveland Ohio
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - John M. Kirkwood
- Department of Medicine and Dermatology, UPMC Hillman Cancer Center and Melanoma and Skin Cancer Program, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Augustin RC, Luke JJ. Rapidly Evolving Pre- and Post-surgical Systemic Treatment of Melanoma. Am J Clin Dermatol 2024; 25:421-434. [PMID: 38409643 DOI: 10.1007/s40257-024-00852-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2024] [Indexed: 02/28/2024]
Abstract
With the development of effective BRAF-targeted and immune-checkpoint immunotherapies for metastatic melanoma, clinical trials are moving these treatments into earlier adjuvant and perioperative settings. BRAF-targeted therapy is a standard of care in resected stage III-IV melanoma, while anti-programmed death-1 (PD1) immunotherapy is now a standard of care option in resected stage IIB through IV disease. With both modalities, recurrence-free survival and distant-metastasis-free survival are improved by a relative 35-50%, yet no improvement in overall survival has been demonstrated. Neoadjuvant anti-PD1 therapy improves event-free survival by approximately an absolute 23%, although improvements in overall survival have yet to be demonstrated. Understanding which patients are most likely to recur and which are most likely to benefit from treatment is now the highest priority question in the field. Biomarker analyses, such as gene expression profiling of the primary lesion and circulating DNA, are preliminarily exciting as potential biomarkers, though each has drawbacks. As in the setting of metastatic disease, markers that inform positive outcomes include interferon-γ gene expression, PD-L1, and high tumor mutational burden, while negative predictors of outcome include circulating factors such as lactate dehydrogenase, interleukin-8, and C-reactive protein. Integrating and validating these markers into clinically relevant models is thus a high priority. Melanoma therapeutics continues to advance with combination adjuvant approaches now investigating anti-PD1 with lymphocyte activation gene 3 (LAG3), T-cell immunoreceptor with Ig and ITIM domains (TIGIT), and individualized neoantigen therapies. How this progress will be integrated into the management of a unique patient to reduce recurrence, limit toxicity, and avoid over-treatment will dominate clinical research and patient care over the next decade.
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Affiliation(s)
- Ryan C Augustin
- UPMC Hillman Cancer Center, 5150 Centre Ave. Room 1.27C, Pittsburgh, PA, 15232, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Jason J Luke
- UPMC Hillman Cancer Center, 5150 Centre Ave. Room 1.27C, Pittsburgh, PA, 15232, USA.
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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Tompkins A, Gray ZN, Dadey RE, Zenkin S, Batavani N, Newman S, Amouzegar A, Ak M, Ak N, Pak TY, Peddagangireddy V, Mamindla P, Behr S, Goodman A, Ploucha DL, Kirkwood JM, Zarour HM, Najjar YG, Davar D, Colen R, Luke JJ, Bao R. Radiomic analysis of patient and inter-organ heterogeneity in response to immunotherapies and BRAF targeted therapy in metastatic melanoma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306411. [PMID: 38712112 PMCID: PMC11071587 DOI: 10.1101/2024.04.26.24306411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Variability in treatment response may be attributable to organ-level heterogeneity in tumor lesions. Radiomic analysis of medical images can elucidate non-invasive biomarkers of clinical outcome. Organ-specific radiomic comparison across immunotherapies and targeted therapies has not been previously reported. Methods We queried UPMC Hillman Cancer Center registry for patients with metastatic melanoma (MEL) treated with immune checkpoint inhibitors (ICI) (anti-PD1/CTLA4 [ipilimumab+nivolumab; I+N] or anti-PD1 monotherapy) or BRAF targeted therapy. Best overall response was measured using RECIST v1.1. Lesions were segmented into discrete volume-of-interest with 400 radiomics features extracted. Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. Results 291 MEL patients were identified, including 242 ICI (91 I+N, 151 PD1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD1) and 126 BRAF. Across cohorts, baseline demographics included 39-47% female, 24-29% M1C, 24-46% M1D, and 61-80% with elevated LDH. Among patients experiencing DC, the organs with the greatest reduction were liver (-88%±12%, I+N; mean±S.E.M.) and lung (-72%±8%, I+N). For patients with multiple same-organ target lesions, the highest inter-lesion heterogeneity was observed in brain among patients who received ICI while no intra-organ heterogeneity was observed in BRAF. 267 patients were kept for radiomic modeling, including 221 ICI (86 I+N, 135 PD1) and 46 BRAF. Models consisting of optimized radiomic signatures classified DC/PD across I+N (AUC=0.85) and PD1 (0.71) and within individual organ sites (AUC=0.72∼0.94). Integration of clinical variables improved the models' performance. Comparison of models between treatments and across organ sites suggested mostly non-overlapping DC or PD features. Skewness, kurtosis, and informational measure of correlation (IMC) were among the radiomic features shared between overall response models. Kurtosis and IMC were also utilized by multiple organ-site models. Conclusions Differential organ-specific response was observed across BRAF and ICI with within organ heterogeneity observed for ICI but not for BRAF. Radiomic features of organ-specific response demonstrated little overlap. Integrating clinical factors with radiomics improves the prediction of disease course outcome and prediction of tumor heterogeneity.
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Crucitta S, Cucchiara F, Marconcini R, Bulleri A, Manacorda S, Capuano A, Cioni D, Nuzzo A, de Jonge E, Mathjissen RHJ, Neri E, van Schaik RHN, Fogli S, Danesi R, Del Re M. TGF-β mRNA levels in circulating extracellular vesicles are associated with response to anti-PD1 treatment in metastatic melanoma. Front Mol Biosci 2024; 11:1288677. [PMID: 38633217 PMCID: PMC11021649 DOI: 10.3389/fmolb.2024.1288677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/27/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction: Immune checkpoint inhibitors (ICIs) represent the standard therapy for metastatic melanoma. However, a few patients do not respond to ICIs and reliable predictive biomarkers are needed. Methods: This pilot study investigates the association between mRNA levels of programmed cell death-1 (PD-1) ligand 1 (PD-L1), interferon-gamma (IFN-γ), and transforming growth factor-β (TGF-β) in circulating extracellular vesicles (EVs) and survival in 30 patients with metastatic melanoma treated with first line anti-PD-1 antibodies. Blood samples were collected at baseline and RNA extracted from EVs; the RNA levels of PD-L1, IFN-γ, and TGF-β were analysed by digital droplet PCR (ddPCR). A biomarker-radiomic correlation analysis was performed in a subset of patients. Results: Patients with high TGF-β expression (cut-off fractional abundance [FA] >0.19) at baseline had longer median progression-free survival (8.4 vs. 1.8 months; p = 0.006) and overall survival (17.9 vs. 2.63 months; p = 0.0009). Moreover, radiomic analysis demonstrated that patients with high TGF-β expression at baseline had smaller lesions (2.41 ± 3.27 mL vs. 42.79 ± 101.08 mL, p < 0.001) and higher dissimilarity (12.01 ± 28.23 vs. 5.65 ± 8.4; p = 0.018). Discussion: These results provide evidence that high TGF-β expression in EVs is associated with a better response to immunotherapy. Further investigation on a larger patient population is needed to validate the predictive power of this potential biomarker of response to ICIs.
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Affiliation(s)
- Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Riccardo Marconcini
- Unit of Medical Oncology 2, Department of Medicine and Oncology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Alessandra Bulleri
- Unit of Radiodiagnostics 1, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Simona Manacorda
- Unit of Medical Oncology 2, Department of Medicine and Oncology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Annalisa Capuano
- Campania Regional Centre for Pharmacovigilance and Pharmacoepidemiology, Section of Pharmacology, Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, Napoli, Italy
| | - Dania Cioni
- Unit of Radiodiagnostics 1, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Amedeo Nuzzo
- Unit of Medical Oncology 2, Department of Medicine and Oncology, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Evert de Jonge
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Ron H. J. Mathjissen
- Department of Medical Oncology, Erasmus University Medical Center Cancer Institute, Rotterdam, Netherlands
| | - Emanuele Neri
- Unit of Radiodiagnostics 1, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Ron H. N. van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Stefano Fogli
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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10
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Sun Z, Zhang T, Ahmad MU, Zhou Z, Qiu L, Zhou K, Xiong W, Xie J, Zhang Z, Chen C, Yuan Q, Chen Y, Feng W, Xu Y, Yu L, Wang W, Yu J, Li G, Jiang Y. Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer. J Clin Invest 2024; 134:e175834. [PMID: 38271117 PMCID: PMC10940098 DOI: 10.1172/jci175834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/22/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.
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Affiliation(s)
- Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Zixia Zhou
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Liang Qiu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Kangneng Zhou
- College of Computer Science, Nankai University, Tianjin, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jingjing Xie
- Graduate Group of Epidemiology, UCD, Davis, California, USA
| | - Zhicheng Zhang
- JancsiTech and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yan Chen
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China
| | - Wanying Feng
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lequan Yu
- The Department of Statistics and Actuarial Science, The University of Hong Kong, HKSAR, Hong Kong, China
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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11
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Rossi E, Boldrini L, Maratta MG, Gatta R, Votta C, Tortora G, Schinzari G. Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study. Hum Vaccin Immunother 2023; 19:2172926. [PMID: 36723981 PMCID: PMC10012916 DOI: 10.1080/21645515.2023.2172926] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.
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Affiliation(s)
- Ernesto Rossi
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Grazia Maratta
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali, Universitá degli Studi di Brescia, Brescia, Italy.,Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Claudio Votta
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giampaolo Tortora
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.,Medical Oncology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Schinzari
- Medical Oncology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.,Medical Oncology, Università Cattolica del Sacro Cuore, Rome, Italy
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12
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Poletto S, Paruzzo L, Nepote A, Caravelli D, Sangiolo D, Carnevale-Schianca F. Predictive Factors in Metastatic Melanoma Treated with Immune Checkpoint Inhibitors: From Clinical Practice to Future Perspective. Cancers (Basel) 2023; 16:101. [PMID: 38201531 PMCID: PMC10778365 DOI: 10.3390/cancers16010101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/11/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The introduction of immunotherapy revolutionized the treatment landscape in metastatic melanoma. Despite the impressive results associated with immune checkpoint inhibitors (ICIs), only a portion of patients obtain a response to this treatment. In this scenario, the research of predictive factors is fundamental to identify patients who may have a response and to exclude patients with a low possibility to respond. These factors can be host-associated, immune system activation-related, and tumor-related. Patient-related factors can vary from data obtained by medical history (performance status, age, sex, body mass index, concomitant medications, and comorbidities) to analysis of the gut microbiome from fecal samples. Tumor-related factors can reflect tumor burden (metastatic sites, lactate dehydrogenase, C-reactive protein, and circulating tumor DNA) or can derive from the analysis of tumor samples (driver mutations, tumor-infiltrating lymphocytes, and myeloid cells). Biomarkers evaluating the immune system activation, such as IFN-gamma gene expression profile and analysis of circulating immune cell subsets, have emerged in recent years as significantly correlated with response to ICIs. In this manuscript, we critically reviewed the most updated literature data on the landscape of predictive factors in metastatic melanoma treated with ICIs. We focus on the principal limits and potentiality of different methods, shedding light on the more promising biomarkers.
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Affiliation(s)
- Stefano Poletto
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Luca Paruzzo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
- Division of Hematology and Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandro Nepote
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Daniela Caravelli
- Medical Oncology Division, Candiolo Cancer Institute, FPO-IRCCs, 10060 Candiolo, Italy; (D.C.); (F.C.-S.)
| | - Dario Sangiolo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
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13
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McGale J, Hama J, Yeh R, Vercellino L, Sun R, Lopci E, Ammari S, Dercle L. Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy. Diagnostics (Basel) 2023; 13:3065. [PMID: 37835808 PMCID: PMC10573034 DOI: 10.3390/diagnostics13193065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.
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Affiliation(s)
- Jeremy McGale
- Department of Radiology, New York-Presbyterian Hospital, New York, NY 10032, USA
| | - Jakob Hama
- Queens Hospital Center, Icahn School of Medicine at Mt. Sinai, Queens, NY 10029, USA
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Laetitia Vercellino
- Nuclear Medicine Department, INSERM UMR S942, Hôpital Saint-Louis, Assistance-Publique, Hôpitaux de Paris, Université Paris Cité, 75010 Paris, France
| | - Roger Sun
- Department of Radiation Oncology, Gustave Roussy, 94800 Villejuif, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS—Humanitas Research Hospital, 20089 Rozzano, MI, Italy
| | - Samy Ammari
- Department of Medical Imaging, BIOMAPS, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France
- ELSAN Department of Radiology, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Laurent Dercle
- Department of Radiology, New York-Presbyterian Hospital, New York, NY 10032, USA
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14
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Guo SB, Pan DQ, Su N, Huang MQ, Zhou ZZ, Huang WJ, Tian XP. Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures. Front Endocrinol (Lausanne) 2023; 14:1266721. [PMID: 37822596 PMCID: PMC10562636 DOI: 10.3389/fendo.2023.1266721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023] Open
Abstract
Background There is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented. Methods After strict quality control, numerous data regarding the lymphoma metabolism were mined, quantified, cleaned, fused, and visualized from documents (n = 2925) limited from 2013 to 2022 using R packages, VOSviewer, and GraphPad Prism. Results The linear fitting analysis generated functions predicting the annual publication number (y = 31.685x - 63628, R² = 0.93614, Prediction in 2027: 598) and citation number (y = 1363.7x - 2746019, R² = 0.94956, Prediction in 2027: 18201). In the last decade, the most academically performing author, journal, country, and affiliation were Meignan Michel (n = 35), European Journal of Nuclear Medicine and Molecular Imaging (n = 1653), USA (n = 3114), and University of Pennsylvania (n = 86), respectively. The hierarchical clustering based on unsupervised learning further divided research signatures into five clusters, including the basic study cluster (Cluster 1, Total Link Strength [TLS] = 1670, Total Occurrence [TO] = 832) and clinical study cluster (Cluster 3, TLS = 3496, TO = 1328). The timeline distribution indicated that radiomics and artificial intelligence (Cluster 4, Average Publication Year = 2019.39 ± 0.21) is a relatively new research cluster, and more endeavors deserve. Research signature burst and linear regression analysis further confirmed the findings above and revealed additional important results, such as tumor microenvironment (a = 0.6848, R² = 0.5194, p = 0.019) and immunotherapy (a = 1.036, R² = 0.6687, p = 0.004). More interestingly, by performing a "Walktrap" algorithm, the community map indicated that the "apoptosis, metabolism, chemotherapy" (Centrality = 12, Density = 6), "lymphoma, pet/ct, prognosis" (Centrality = 11, Density = 1), and "genotoxicity, mutagenicity" (Centrality = 9, Density = 4) are crucial but still under-explored, illustrating the potentiality of these research signatures in the field of the lymphoma metabolism. Conclusion This study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice.
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Affiliation(s)
- Song-Bin Guo
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Dan-Qi Pan
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Ning Su
- Department of Oncology, Guangzhou Chest Hospital, Guangzhou, China
| | - Man-Qian Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhen-Zhong Zhou
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei-Juan Huang
- Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China
| | - Xiao-Peng Tian
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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15
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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