Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs). A total of 730 eligible patients with 2722 lesions were included. The DLM reached an area under the receiver operating characteristic (AUROC) of 0.607 [95%CI 0.565-0.648]. In comparison, a model of clinical predictors reached an AUROC of 0.635 [95%CI 0.59 -0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595-0.676]. Differences in AUROC were not statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model. The DLM reached a statistically significant discriminative value, but was unable to improve over known clinical predictors. The present work shows that the assessment over known clinical predictors is an essential step for imaging-based prediction and brings important nuance to the almost exclusively positive findings in this field.

Longitudinal Body Composition Identifies Hepatocellular Carcinoma With Cachexia Following Combined Immunotherapy and Target Therapy (CHANCE2213)

BACKGROUND

Cancer cachexia can impact prognosis, cause resistance to anticancer treatments and affect the tolerability of treatments. This study aims to identify hepatocellular carcinoma (HCC) with cachexia by characterizing longitudinal body composition (BC) trajectories.

METHODS

This longitudinal, multicentre cohort study included unresectable HCC patients treated with first-line programmed death-(ligand)1 inhibitors plus anti-vascular endothelial growth factor antibody/tyrosine kinase inhibitors between 01/2018-12/2022. BC measurements including skeletal muscle mass (SMM) and total adipose tissue area (TATA) were evaluated by computed tomography at the third lumbar vertebra at baseline and follow-up imaging. Unsupervised latent class growth mixed models were applied to distinguish potential longitudinal SMM and TATA trajectories for identifying cachexia. The primary study endpoint was overall survival (OS), with secondary endpoints including progression-free survival (PFS), objective response rate (ORR) and safety. Multiple Cox proportional hazards models were used to calculate adjusted hazard ratios (HRs) for survival.

RESULTS

A total of 411 patients with 2138 time-point measurements were included. The median age was 56 years, and 50 (12.2%) patients were female. Two distinct trajectories were identified for SMM and TATA: sharp-falling and stable. SMM sharply declined in 58 patients (14.1%) and TATA in 71 of 406 patients (17.5%) with significant worse OS (for SMM, 17.0 vs. 24.9 months; p < 0.001; HR = 0.59; for TATA, 15.3 vs. 25.1 months; p < 0.001; HR = 0.44). Patients were categorized into three phases based on trajectories: pre-cachexia (SMM and TATA stable, n = 299, 73.6%), cachexia (SMM or TATA sharp-falling, n = 86, 21.2%) and refractory cachexia (SMM and TATA sharp-falling, n = 21, 5.2%). Patients with refractory cachexia exhibited the worst OS, PFS and ORR, followed by those with cachexia. The median OS was 11.5 months for refractory cachexia, 17.7 for cachexia and 26.0 for pre-cachexia; median PFS was 6.0, 7.9 and 10.9 months, respectively, with ORR of 4.8%, 39.5% and 54.2%, respectively (all ps < 0.001). Multivariable Cox analysis identified refractory cachexia as an independent risk factor for both OS (HR = 3.31; p < 0.001) and PFS (HR = 2.94; p < 0.001), with cachexia also showing significant impacts. Grade 3-4 adverse events were higher in patients with refractory cachexia (23.8%) and cachexia (8.1%) compared with pre-cachexia (6.0%; p = 0.010).

CONCLUSIONS

HCC patients with cachexia and refractory cachexia were identified by longitudinal BC trajectories. Falling trajectories of BC identified refractory cachexia patients with worst response, survival and poor tolerability from systemic therapy combinations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT05278195.

Chest computed tomography for patients with sepsis in the emergency intensive care unit

Sepsis is a systemic inflammatory response syndrome (SIRS) caused by infection, which may lead to multiple organ dysfunction in susceptible patient. The most frequently involved organs/systems include the lung, kidney and circulation system. It is well established that sepsis is a risk factor for acute lung injury. While overt pulmonary infiltrates can be well captured by human operators, subtle structural changes of the lung might be ignored. Since the advantage of chest computed tomography (CT) is its capability of providing fine structural changes in high spatial resolution, the study of chest CT by means of computer science may provide further insights into the underlying pathophysiology. The integration of chest CT into the study of sepsis is limited partly due to the lack of well-curated database. The study aims to establish a database comprising detailed clinical tabular data, as well as the raw chest CT images. The database is intended to support a wide array of research studies involving radiomics in sepsis patients, helping to reduce barriers to the reproducibility of clinical research.

Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases

Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung (n = 20 334), lymph nodes (n = 11 651), and liver (n = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model (R2 = 0.17; ρ = 0.41; P < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm3 for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm3. Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Koh in this issue.

The Emerging Role of Immune Checkpoint Blockade for the Treatment of Lung Cancer Brain Metastases

Lung cancer has the highest incidence of brain metastases (BM) among solid organ cancers. Traditionally whole brain radiation therapy has been utilized for non-small-cell lung cancer (NSCLC) BM treatment, although stereotactic radiosurgery has emerged as the superior treatment modality for most patients. Highly penetrant central nervous system (CNS) tyrosine kinase inhibitors have also shown significant CNS activity in patients harboring select oncogenic drivers. There is emerging evidence that patients without oncogene-driven tumors derive benefit from the use of immune checkpoint inhibitors (ICIs). The CNS activity of ICIs have not been well studied given exclusion of patients with active BM from landmark trials, due to concerns of inadequate CNS penetration and activity. However, studies have challenged the idea of an immune-privileged CNS, given the presence of functional lymphatic drainage within the CNS and destruction of the blood brain barrier by BM. An emerging understanding of the interactions between tumor and CNS immune cells in the BM tumor microenvironment also support a role for immunotherapy in BM treatment. In addition, posthoc analyses of major trials have shown improved intracranial response and survival benefit of regimens with ICIs over chemotherapy (CT) alone for patients with BM. Two prospective phase 2 trials evaluating pembrolizumab monotherapy and atezolizumab plus CT in patients with untreated NSCLC BM also demonstrated significant intracranial responses. This review describes the interplay between CNS immune cells and tumor cells, discusses current evidence for ICI CNS activity from retrospective and prospective studies, and speculates on future directions of investigation.

A prediction model for response to immune checkpoint inhibition in advanced melanoma

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.

Historical perspective and future directions: computational science in immuno-oncology

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.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

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.

Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review

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).

Clinical trials of neoadjuvant immune checkpoint inhibitors for early-stage operable colon and rectal cancer

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.

Site of distant metastasis affects the prognosis with recurrent/metastatic head and neck squamous cell carcinoma patients treated with Nivolumab

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.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

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.

CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma

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.

Immunotherapy Assessment: A New Paradigm for Radiologists

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.