Systemic immunotherapies such as immune checkpoint blockade targeted at PD(L)1 and CTLA4 have demonstrated their ability to provide durable tumor responses and long-term overall survival benefits for some patients in several solid tumor types. However, a majority of patients remain resistant to these treatments and a significant proportion of them develop severe autoimmune and inflammatory adverse events. Preclinical studies have demonstrated that intratumoral injections of immunostimulatory products (oncolytics, pattern recognition receptor agonists,…) that are able to trigger type I IFN release and enhance tumor antigen presentation on immune cells could generate a strong antitumor immunity and overcome the resistance to systemic immune checkpoint blockade therapies. The intratumoral immunotherapy strategies that are currently in clinical development offer a unique therapeutic and exploratory setting to better understand the immune contexture across tumor lesions of patients with metastatic cancer. Also these local therapeutic products could turn cold tumors into hot and improve the response rates to cancer immunotherapies while diminishing their systemic exposure and toxicities. Intratumoral immunotherapies could prime or boost the immunity against tumors and therefore radically change the combinatorial therapeutic strategies currently pursued for metastatic and local cancers to improve their long-term survival. We aimed to review and discuss the scientific rationale for intratumoral immunotherapy, the challenges raised by this strategy in terms of drug development within clinical trials and the current state-of-the-art regarding the clinical practice of this innovative approach.

Purpose: Anti–PD-(L)1 can provide overall survival (OS) benefits over conventional treatments for patients with many different cancer types. However, the long-term outcome of cancer patients responding to these therapies remains unknown. This study is an exploratory study that aimed to describe the long-term survival of patients responding to anti–PD-(L)1 monotherapy across multiple cancer types. Patients and Methods: Data from patients treated with an anti–PD-(L)1 monotherapy in a phase I trial at Gustave Roussy were retrospectively analyzed over a period of 5 years. All cancer types (n = 19) were included. Clinical and biological factors associated with response, long-term survival, and secondary refractory disease were studied. Results: Among 262 eligible patients, the overall objective response rate was 29%. The median progression-free survival of responder patients (RP) at 3 months was 30 months, and the median OS of RP was not reached after a median follow-up of 34 months. In RPs, 3- and 5-year OS percentages were 84% and 64%, respectively. No death occurred in the 21 complete responders (CR) during the overall follow-up. However, many partial responders (PR) showed subsequent tumor relapses to treatment. Long responders (response ≥2 years) represented 11.8% of the overall population. These findings should be validated in further prospective studies. Conclusions: There are currently no differences in therapeutic strategies between CRs and PRs to anti–PD-(L)1. We found a striking difference in OS between these two types of responses. Our results are in favor of evaluating patient stratification strategies and intensification of treatments when tumor lesions of a partial responder to immunotherapy stop improving. See related commentary by Cohen and Flaherty, p. 910

In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.

Background: The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice. Methods: T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.5 and 3T). Phantoms varied in terms of gadolinium concentrations and textural heterogeneity. 27 regions of interest were segmented (phantom: 21, volunteers: 6) using the LIFEX software. 34 features were analyzed. Results: In the phantom dataset, 10 (67%) out of 15 radiomics features were significantly different when measured at 1.5T or 3T (student’s t-test, p < 0.05). Gray levels resampling, and pixel size also influence part of texture features. These findings were validated in healthy volunteers. Conclusions: According to daily used protocols for clinical examinations, radiomic features extracted on 1.5T should not be used interchangeably with 3T when evaluating texture features. Such confounding factor should be adjusted when adapting the results of a study to a different platform, or when designing a multicentric trial.

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

This review highlights the distinct imaging patterns associated with immunotherapy response and progression and considers novel imaging approaches to evaluate emerging immunotherapies. Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immun...