1
|
van Duin IAJ, Verheijden RJ, van Diest PJ, Blokx WAM, El-Sharouni MA, Verhoeff JJC, Leiner T, van den Eertwegh AJM, de Groot JWB, van Not OJ, Aarts MJB, van den Berkmortel FWPJ, Blank CU, Haanen JBAG, Hospers GAP, Piersma D, van Rijn RS, van der Veldt AAM, Vreugdenhil G, Wouters MWJM, Stevense-den Boer MAM, Boers-Sonderen MJ, Kapiteijn E, Suijkerbuijk KPM, Elias SG. A prediction model for response to immune checkpoint inhibition in advanced melanoma. Int J Cancer 2024; 154:1760-1771. [PMID: 38296842 DOI: 10.1002/ijc.34853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/01/2023] [Accepted: 12/05/2023] [Indexed: 02/02/2024]
Abstract
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.
Collapse
Affiliation(s)
- Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik J Verheijden
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Willeke A M Blokx
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mary-Ann El-Sharouni
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tim Leiner
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Alfonsus J M van den Eertwegh
- Department of Medical Oncology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Olivier J van Not
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Scientific Bureau, Dutch Institute for Clinical Auditing, Leiden, The Netherlands
| | - Maureen J B Aarts
- Department of Medical Oncology, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Christian U Blank
- Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - John B A G Haanen
- Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Geke A P Hospers
- Department of Medical Oncology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Djura Piersma
- Department of Internal Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Rozemarijn S van Rijn
- Department of Internal Medicine, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Astrid A M van der Veldt
- Department of Medical Oncology and Radiology & Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Gerard Vreugdenhil
- Department of Internal Medicine, Maxima Medical Centre, Eindhoven, The Netherlands
| | - Michel W J M Wouters
- Scientific Bureau, Dutch Institute for Clinical Auditing, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Marye J Boers-Sonderen
- Department of Medical Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Ellen Kapiteijn
- Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| |
Collapse
|
2
|
Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
Collapse
Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | | |
Collapse
|
3
|
Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
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. RESULTS The 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.
Collapse
Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| |
Collapse
|
4
|
Pei X, Xie Y, Liu Y, Cai X, Hong L, Yang X, Zhang L, Zhang M, Zheng X, Ning K, Fang M, Tang H. Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review. Front Oncol 2023; 13:1198723. [PMID: 37916163 PMCID: PMC10616831 DOI: 10.3389/fonc.2023.1198723] [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: 04/02/2023] [Accepted: 10/02/2023] [Indexed: 11/03/2023] Open
Abstract
Background Since the application of Immune checkpoint inhibitors (ICI), the clinical outcome for metastatic cancer has been greatly improved. Nevertheless, treatment response varies in patients, making it urgent to identify patients who will receive clinical benefits after ICI therapy. Adipose body composition has proved to be associated with tumor response. In this systematic review, we aimed to summarize the current evidence on imaging adipose biomarkers that predict clinical outcomes in patients treated with ICI in various cancer types. Methods Embase and PubMed were searched from database inception to 1st February 2023. Articles included investigated the association between imaging-based adipose biomarkers and the clinical outcomes of patients treated with ICI. The methodological quality of included studies was evaluated through Newcastle- Ottawa Quality Assessment Scale and Radiomics Quality Score tools. Results Totally, 22 studies including 2256 patients were selected. Non-small cell lung cancer (NSCLC) had the most articles (6 studies), followed by melanoma (5 studies), renal cell carcinoma (RCC) (3 studies), urothelial carcinoma (UC) (2 studies), head and neck squamous cell carcinoma (HNSCC) (1 study), gastric cancer (1 study) and liver cancer (1 study). The remaining 3 studies investigated metastatic solid tumors including various types of cancers. Adipose biomarkers can be summarized into 5 categories, including total fat, visceral fat, subcutaneous fat, intramuscular fat and others, which exerted diverse correlations with patients' prognosis after being treated with ICI in different cancers. Most biomarkers of body fat were positively associated with survival benefits. Nevertheless, more total fat was predictable of worse outcomes in NSCLC, while inter-muscular fat was associated with poor clinical benefits in UC. Conclusion There is relatively well-supported evidence for imaging-based adipose biomarkers to predict the clinical outcome of ICI. In general, most of the studies show that adipose tissue is positively correlated with clinical outcomes. This review summarizes the significant biomarkers proven by researches for each cancer type. Further validation and large independent prospective cohorts are needed in the future. The protocol of this systematic review has been registered at the International Prospective Register of Systematic Reviews (http://www.crd.york.ac.uk/PROSPERO, registration no: CRD42023401986).
Collapse
Affiliation(s)
- Xinyu Pei
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ye Xie
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yixuan Liu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinyang Cai
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Lexuan Hong
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaofeng Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Luyao Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Manhuai Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinyi Zheng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Kang Ning
- Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
- State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Mengyuan Fang
- Department of Ultrasound, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China
| | - Huancheng Tang
- Department of Urology, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
5
|
Veen T, Kanani A, Lea D, Søreide K. Clinical trials of neoadjuvant immune checkpoint inhibitors for early-stage operable colon and rectal cancer. Cancer Immunol Immunother 2023; 72:3135-3147. [PMID: 37528319 PMCID: PMC10491705 DOI: 10.1007/s00262-023-03480-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/08/2023] [Indexed: 08/03/2023]
Abstract
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.
Collapse
Affiliation(s)
- Torhild Veen
- Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Arezo Kanani
- Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Dordi Lea
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
| | - Kjetil Søreide
- Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway.
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway.
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| |
Collapse
|
6
|
Hoshi Y, Shirakura S, Yamada M, Sugiyama T, Koide N, Tamii S, Kamata K, Yokomura M, Osaki S, Ohno T, Yagihara K, Hara H, Beppu T. Site of distant metastasis affects the prognosis with recurrent/metastatic head and neck squamous cell carcinoma patients treated with Nivolumab. Int J Clin Oncol 2023; 28:1139-1146. [PMID: 37421478 DOI: 10.1007/s10147-023-02381-3] [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: 03/24/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND Nivolumab is approved for the treatment of recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). However, the influence of the site of distant metastasis on the efficacy of immune checkpoint inhibitor in R/M HNSCC remains unclear. We investigated the prognosis of R/M HNSCC patients treated with nivolumab, focusing on the site of distant metastasis. METHODS We reviewed the data of R/M HNSCC patients treated with nivolumab between April 2017 and June 2020 at Saitama Prefectural Cancer Center. The differences in the prognosis were evaluated according to the site of distant metastasis. RESULTS Of the 41 patients enrolled, 26 (63.4%) had lung metastasis, 7 (17.1%) had bone metastasis, and 4 (9.8%) had liver metastasis. Ten patients (24.4%) had single-organ distant metastasis (lung metastasis in all cases). Univariate analysis identified lung metastasis alone (single-organ distant metastasis) was associated with a significantly better prognosis [HR0.37 (95% CI) 0.14-0.97 p = 0.04], while liver metastasis was associated with a significantly worse prognosis [HR3.86 (95% CI) 1.26-11.8 p = 0.02]. Multivariate analysis identified lung metastasis alone and liver metastasis as independent prognostic factors. While 7 patients (70%) with lung metastasis alone could be continued on nivolumab treatment or received subsequent chemotherapy, only 1 patient (25%) with liver metastasis received subsequent chemotherapy. CONCLUSION The site of distant metastasis affects the prognosis of R/M HNSCC patients treated with nivolumab. Lung metastasis alone appears to be associated with a better prognosis, in that it allows easier transition to subsequent chemotherapy, while liver metastasis associates with a worse prognosis.
Collapse
Affiliation(s)
- Yuta Hoshi
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan.
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
| | - Satoshi Shirakura
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Masato Yamada
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Tomonori Sugiyama
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
| | - Nobuaki Koide
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Satoru Tamii
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
| | - Kyohei Kamata
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Otolaryngology, Head and Neck Surgery, Yamagata University Faculty of Medicine, 1-4-12 Kojirakawa-machi, Yamagata-shi, Yamagata, 990-8560, Japan
| | - Masaru Yokomura
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Sotaro Osaki
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Takafumi Ohno
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Kazuhiro Yagihara
- Department of Oral Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
| | - Hiroki Hara
- Department of Gastroenterology, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
| | - Takeshi Beppu
- Department of Head and Neck Surgery, Saitama Prefectural Cancer Center, 780 Komuro, Ina-Machi, Kita-Adachi-Gun, Saitama, 362-0806, Japan
- Department of Head and Neck Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| |
Collapse
|
7
|
Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [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: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| |
Collapse
|
8
|
ter Maat L, van Duin IA, Elias SG, Leiner T, Verhoeff JJ, Arntz ER, Troenokarso MF, Blokx WA, Isgum I, de Wit GA, van den Berkmortel FW, Boers-Sonderen MJ, Boomsma MF, van den Eertwegh FJ, de Groot JWB, Piersma D, Vreugdenhil A, Westgeest HM, Kapiteijn E, van Diest PJ, Pluim J, de Jong PA, Suijkerbuijk KP, Veta M. CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma. Eur J Cancer 2023; 185:167-177. [PMID: 36996627 DOI: 10.1016/j.ejca.2023.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 03/18/2023]
Abstract
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 tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. METHODS Patients who received first-line anti-PD1±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 clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-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 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 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.
Collapse
|
9
|
Granata V, Fusco R, Setola SV, Simonetti I, Picone C, Simeone E, Festino L, Vanella V, Vitale MG, Montanino A, Morabito A, Izzo F, Ascierto PA, Petrillo A. Immunotherapy Assessment: A New Paradigm for Radiologists. Diagnostics (Basel) 2023; 13:diagnostics13020302. [PMID: 36673112 PMCID: PMC9857844 DOI: 10.3390/diagnostics13020302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/31/2022] [Accepted: 01/08/2023] [Indexed: 01/14/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Carmine Picone
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Ester Simeone
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Lucia Festino
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Vito Vanella
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Maria Grazia Vitale
- Melanoma, Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, 80131 Naples, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Alessandro Morabito
- Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Paolo Antonio Ascierto
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| |
Collapse
|