Objective response rate assessment in oncology: Current situation and future expectations

High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades

BACKGROUND

Clinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades.

METHODS

Twenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades.

RESULTS

High-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037).

CONCLUSIONS

Both high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03741426 . Registered on 13 November 2018.

RELEVANCE STATEMENT

The newly developed T2 mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.

Patient eligibility for trials with imaging response assessment at the time of molecular tumor board presentation

PURPOSE

To assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1).

METHODS

Prospective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden.

RESULTS

262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%).

CONCLUSION

Approximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

Correlation between surrogate endpoints and overall survival in unresectable hepatocellular carcinoma patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

This study aimed to assess the therapeutic effect of immune checkpoint inhibitors (ICIs) in patients with unresectable hepatocellular carcinoma (uHCC) and investigate the correlation between surrogate endpoints and overall survival (OS). A systematic literature search included phase I, II, and III clinical trials comparing ICIs to placebo or other therapies for uHCC treatment. Correlations between OS and surrogate endpoints were evaluated using meta-regression analyses and calculating the surrogate threshold effect (STE). The correlation analysis showed a weak association between OS and progression-free survival (PFS), with an R2 value of 0.352 (95% CI: 0.000-0.967). However, complete response (CR) exhibited a strong correlation with OS (R2 = 0.905, 95% CI: 0.728-1.000). Subgroup analyses revealed high correlations between OS and PFS, CR, stable disease (SD), and DC in phase III trials (R2: 0.827-0.922). For the ICI + IA group, significant correlations were observed between OS and SD, progressive disease (PD), and grade 3-5 immune-related adverse events (irAEs) (R2: 0.713-0.969). Analyses of the correlation between survival benefit and risk of mortality across various time points showed a strong association within the first year (R2: 0.724-0.868) but a weak association beyond one year (R2: 0.406-0.499). In ICI trials for uHCC, PFS has limited utility as a surrogate endpoint for OS, while CR exhibits a strong correlation with OS. Subgroup analyses highlight high correlations between OS and PFS, SD, and DC in phase III trials. Notably, the ICI + IA group shows significant associations between OS and SD, PD, and grade 3-5 irAEs. These findings offer valuable insights for interpreting trial outcomes and selecting appropriate endpoints in future clinical studies involving ICIs for uHCC patients.

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer

Aim

To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM).

Methods

From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves.

Results

Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5-103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88.

Conclusions

MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM.

A Comprehensive Review of Current Trends in the Diagnosis and Treatment of Ovarian Germ Cell Tumors

Ovarian germ cell tumors constitute a rare and intricate spectrum of neoplasms characterized by diverse histological subtypes. This comprehensive review elucidates the classification, diagnosis, treatment, prognosis, and unique challenges associated with these tumors. The classification is rooted in histological attributes, with principal subtypes encompassing dysgerminoma, immature teratoma, yolk sac tumor (endodermal sinus tumor), choriocarcinoma, and mixed germ cell tumors. Each subtype bears distinct characteristics and clinical implications, necessitating precise diagnosis and tailored therapeutic strategies. Diagnosis hinges upon recognizing the broad clinical presentation, employing imaging techniques (such as ultrasound and MRI), evaluating tumor markers (alpha-fetoprotein and beta-human chorionic gonadotropin), and conducting histopathological examinations where necessary. Staging, primarily utilizing the International Federation of Gynecology and Obstetrics (FIGO) system, is pivotal in determining the extent of disease, guiding treatment choices, and facilitating prognostic assessment. Treatment modalities encompass surgery, chemotherapy (including standard regimens and emerging therapies), radiation therapy, targeted therapies, and immunotherapy. Prognosis is influenced by histological subtype, tumor stage, patient age, surgical success, response to chemotherapy, and tumor markers, while predictive biomarkers are continually emerging. Despite advances in treatment, ovarian germ cell tumors pose distinct challenges, including late diagnosis, treatment-related side effects, and the enigma of chemoresistance. An integral aspect of comprehensive care is supportive strategies to manage symptoms and offer psychological and emotional support. This review accentuates the vital role of early diagnosis and multidisciplinary care in optimizing outcomes. Future research directions and evolving clinical practices are explored in these intricate and distinctive malignancies, highlighting the dynamic landscape of ovarian germ cell tumors.

Prognostic value of blood-based protein biomarkers in non-small cell lung cancer: A critical review and 2008-2022 update

BACKGROUND

Therapeutic possibilities for non-small cell lung cancer (NSCLC) have considerably increased during recent decades.

OBJECTIVE

To summarize the prognostic relevance of serum tumor markers (STM) for early and late-stage NSCLC patients treated with classical chemotherapies, novel targeted and immune therapies.

METHODS

A PubMed database search was conducted for prognostic studies on carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase, squamous-cell carcinoma antigen, progastrin-releasing-peptide, CA125, CA 19-9 and CA 15-3 STMs in NSCLC patients published from 2008 until June 2022.

RESULTS

Out of 1069 studies, 141 were identified as meeting the inclusion criteria. A considerable heterogeneity regarding design, patient number, analytical and statistical methods was observed. High pretherapeutic CYFRA 21-1 levels and insufficient decreases indicated unfavorable prognosis in many studies on NSCLC patients treated with chemo-, targeted and immunotherapies or their combinations in early and advanced stages. Similar results were seen for CEA in chemotherapy, however, high pretherapeutic levels were sometimes favorable in targeted therapies. CA125 is a promising prognostic marker in patients treated with immunotherapies. Combinations of STMs further increased the prognostic value over single markers.

CONCLUSION

Protein STMs, especially CYFRA 21-1, have prognostic potential in early and advanced stage NSCLC. For future STM investigations, better adherence to comparable study designs, analytical methods, outcome measures and statistical evaluation standards is recommended.

A review of targeted therapy and immune checkpoint inhibitors for metastatic colorectal cancer

Surgical resection is the cornerstone of treatment for metastatic colorectal cancer (CRC) and offers the best chance at long-term survival. Unfortunately, most patients do not present with resectable metastatic disease and, among patients who do undergo curative-intent resection, many will develop recurrence. In turn, patients require a multi-disciplinary treatment approach with a combination of chemotherapy, surgery, radiation, and/or liver directed therapies that is guided by patient disease burden and clinical status. The development of targeted therapies has led to varying success in other cancers and has emerged as a treatment option for patients with metastatic CRC. While cytotoxic chemotherapy aims to kill cells as they replicate, targeted therapies are directed at biologic features of cancers, like angiogenesis or immune checkpoints. Targeted therapy can facilitate a more treatment tailored approach to the unique genomic alterations of the tumor and hopefully deliver more personalized therapy. We herein provide a systematic review of approved targeted therapies and immune checkpoint inhibitors for metastatic CRC and provide an overview of the current literature.

Systemic delivery of glycosylated-PEG-masked oncolytic virus enhances targeting of antitumor immuno-virotherapy and modulates T and NK cell infiltration

Rationale: Immuno-virotherapy has emerged as a promising approach for cancer treatment, as it directly and cytotoxically eliminates tumors with systemic immune stimulation. However, the clinical efficacy of this approach remains limited by inappropriate delivery routes, robust antiviral responses, and the tumor immunosuppressive microenvironment. Methods: To address these challenges, we propose a surface engineering strategy that masks oncolytic herpes simplex virus (oHSV) with a galactose-polyethylene-glycol (PEG) polymer chain to minimize host antiviral responses and selectively targets tumors by limiting exposure to circulation upon systemic administration. We evaluated the antitumor efficacy of glycosylated-PEG-oHSV by examining tumor growth in animal models and analyzing tumor-infiltrating CD8+T cells and NK cells in the tumor microenvironment (TME). To assess the neutralizing antibody levels after systemic administration of glycosylated-PEG-oHSV, we utilized a mouse model and measured oHSV-specific IgG. Results: We demonstrate that the glycosylated-PEG modified oHSV does not affect the replication of oHSV yet exhibits high specificity to the asialoglycoprotein receptor (ASGPR) overexpressed in hepatocellular carcinoma cells. This results in selectively targeting cancer cells and deep penetration into tumors while avoiding spreading into the brain. Our approach also effectively reduces oHSV-specific neutralizing antibody levels to mitigate host antiviral immune response. Notably, our glycosylated-PEG-oHSV alleviates the immunosuppressive microenvironment within tumors by reducing regulatory T cells, augmenting the infiltration of activated CD8+T cells and NK cells with increasing release of anti-tumor cytokines, to impede tumor progression. Conclusion: Our findings offer a widely applicable and universal strategy to enhance cancer immuno-virotherapy through systemic administration of non-genetically engineered oncolytic viruses. This approach has the potential to overcome the limitations of current immune-virotherapy strategies and may improve clinical outcomes for cancer patients.

Mutant HRas Signaling and Rationale for Use of Farnesyltransferase Inhibitors in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are often associated with poor outcomes, due at least in part to the limited number of treatment options available for those patients who develop recurrent and/or metastatic disease (R/M HNSCC). Even with the recent validation and approval of immunotherapies in the first-line setting for these patients, the need for the development of new and alternative precision medicine strategies with survival benefit is clear. Oncogenic alterations in the HRAS (Harvey rat sarcoma virus) proto-oncogene are seen in approximately 4-8% of R/M HNSCC tumors. Recently, several preclinical and clinical advancements have been made in the implementation of small-molecule inhibitors that block post-translational farnesylation of HRas, thereby abrogating its downstream oncogenic activity. In this review, we focus on the biology of wild-type and mutant HRas signaling in HNSCC, and rationale for use and outcomes of farnesyltransferase inhibitors in patients with HRAS-mutant tumors.

PTPRZ1-METFUsion GENe (ZM-FUGEN) trial: study protocol for a multicentric, randomized, open-label phase II/III trial

BACKGROUND

PTPRZ1-MET fusion was reported to associate with glioma progression from low-grade to high-grade glioma, which was a target by a MET inhibitor vebreltinib. However, little is known about the further efficacy of vebreltinib among more glioma patients. This trial aims to evaluate the safety and efficacy of vebreltinib enteric-coated capsules in the treatment of sGBM/IDH mutant glioblastoma patients with the ZM fusion gene.

METHODS

This multicentric, randomized, open-label, controlled trial plans to include 19 neurosurgical centers and recruit 84 sGBM or IDH mutant glioblastoma patients with the ZM fusion gene. This trial enrolls sGBM or IDH mutant glioblastoma patients with the inclusion criteria and without the exclusion criteria. It was registered with chinadrugtrials.org.cn (CTR20181664). The primary efficacy endpoint is overall survival (OS). The secondary endpoints are progression-free survival (PFS) and objective response rate (ORR).

DISCUSSION

If proven effective, this targeted multifaceted intervention protocol will be extended for more glioma patients as a protocol to evaluate the safety and efficacy of MET inhibitors.

TRIAL REGISTRATION

It was registered with chinadrugtrials.org.cn (CTR20181664).

Development and Validation of Blood-Based Predictive Biomarkers for Response to PD-1/PD-L1 Checkpoint Inhibitors: Evidence of a Universal Systemic Core of 3D Immunogenetic Profiling across Multiple Oncological Indications

BACKGROUND

Unprecedented advantages in cancer treatment with immune checkpoint inhibitors (ICIs) remain limited to only a subset of patients. Systemic analyses of the regulatory 3D genome architecture linked to individual epigenetic and immunogenetic controls associated with tumour immune evasion mechanisms and immune checkpoint pathways reveal a highly prevalent molecular profile predictive of response to PD-1/PD-L1 ICIs. A clinical blood test based on a set of eight (8) 3D genomic biomarkers has been developed and validated on the basis of an observational trial to predict response to ICI therapy.

METHODS

The predictive eight biomarker set is derived from prospective observational clinical trials, representing 280 treatments with Pembrolizumab, Atezolizumab, Durvalumab, Nivolumab, and Avelumab in a broad range of indications: melanoma, lung, hepatocellular, renal, breast, bladder, colon, head and neck, bone, brain, lymphoma, prostate, vulvar, and cervical cancers.

RESULTS

The 3D genomic eight biomarker panel for response to immune checkpoint therapy achieved a high accuracy of 85%, sensitivity of 93%, and specificity of 82%.

CONCLUSIONS

This study demonstrates that a 3D genomic approach can be used to develop a predictive clinical assay for response to PD-1/PD-L1 checkpoint inhibition in cancer patients.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features

PURPOSE

To elucidate the role of atrial anatomical remodeling in atrial fibrillation (AF), we proposed an automatic method to extract and analyze morphological characteristics in left atrium (LA), left atrial appendage (LAA) and pulmonary veins (PVs) and constructed classifiers to evaluate the importance of identified features.

METHODS

The LA, LAA and PVs were segmented from contrast computed tomography images using either a commercial software or a self-adaptive algorithm proposed by us. From these segments, geometric and fractal features were calculated automatically. To reduce the model complexity, a feature selection procedure is adopted, with the important features identified via univariable analysis and ensemble feature selection. The effectiveness of this approach is well illustrated by the high accuracy of our models.

RESULTS

Morphological features, such as LAA ostium dimensions and LA volume and surface area, statistically distinguished ([Formula: see text]) AF patients or AF with LAA filling defects (AF(def+)) patients among all patients. On the test set, the best model to predict AF among all patients had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI, 0.8-1) and the best model to predict AF(def+) among all patients had an AUC of 0.92 (95% CI, 0.81-1).

CONCLUSION

This study automatically extracted and analyzed atrial morphology in AF and identified atrial anatomical remodeling that statistically distinguished AF or AF(def+). The importance of identified atrial morphological features in characterizing AF or AF(def+) was validated by corresponding classifiers. This work provides a good foundation for a complete computer-assisted diagnostic workflow of predicting the occurrence of AF or AF(def+).

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Chronomodulated Administration of Chemotherapy in Advanced Colorectal Cancer: A Systematic Review and Meta-Analysis

In this systematic review, the efficacy and safety of chronomodulated chemotherapy, defined as the delivery of chemotherapy timed according to the human circadian rhythm, were assessed and compared to continuous infusion chemotherapy for patients with advanced colorectal cancer. Electronic English-language studies published until October 2020 were searched. Randomised controlled trials (RCTs) comparing chronomodulated chemotherapy with non-chronomodulated (conventional) chemotherapy for the management of advanced colorectal cancer were included. The main outcomes were the objective response rate (ORR) and system-specific and overall toxicity related to chemotherapy. Electronic databases including Ovid Medline, Ovid Embase, Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Review were searched. In total, seven RCTs including 1,137 patients were analysed. Males represented 684 (60%) of the study population. The median age was 60.5 (range = 47.2-64) years. There was no significant difference between chronomodulated and conventional chemotherapy in ORR (risk ratio (RR) = 1.15; 95% confidence interval (CI) = 0.87-1.53). Similarly, there was no significant difference in gastrointestinal toxicity under the random effect model (RR = 1.02; 95% CI = 0.68-1.51). No significant difference was found regarding neurological and skin toxicities (RR = 0.64, 95% CI = 0.32-1.270 and RR = 2.11, 95% CI = 0.33-13.32, respectively). However, patients who received chronomodulated chemotherapy had less haematological toxicity (RR = 0.36, 95% CI = 0.27-0.48). In conclusion, there was no overall difference in ORR or haematologic toxicity between chronomodulated and non-chronomodulated chemotherapy used for patients with advanced colorectal cancer. Chronomodulated chemotherapy can be considered in patients at high risk of haematological toxicities.

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

The efficacy of 18F-FDG PET/CT in monitoring disease progression in malignant pleural mesothelioma

OBJECTIVE

In the event of suspicion of malignant pleural mesothelioma (MPM) progression, imaging plays an important role. We aimed to evaluate the efficacy of ¹⁸F-FDG PET/CT in monitoring disease progression by comparing it with CT, and estimate median overall survival (OS) according to progression status with CT and ¹⁸F-FDG PET/CT.

MATERIALS AND METHODS

This was an observational, retrospective, single-institution study with MPM patients who had both ¹⁸F-FDG PET/CT and CT for monitoring disease progression from March 2009 to February 2020. Clinical features, radiological findings, and progression status according to CT [radiologic progression negative (RPN), radiologic progression positive (RPP)] and ¹⁸F-FDG PET/CT [metabolic progression negative (MPN), metabolic progression positive (MPP)] were recorded. The discrepancies and concordance between two methods were evaluated. The OS was estimated using the Kaplan-Meier method.

RESULTS

A total of 56 patients were included. There were thirty-one (55.3%) RPN and 25 (44.7%) RPP, while there were 26 (46.5%) MPN and 30 (53.5%) MPP. All RPP patients were also found to be MPP, however, among RPN, 5 patients (8.9% of all patients) were evaluated as MPP. The concordance between two methods in monitoring disease progression was very good (K = 0.423; p < 0.01). The OS was 26 ± 2.6 months in all patients. Kaplan-Meier curves between RPN and RPP, and between MPN and MPP did not show statistically significant differences (p = 0.56 and p = 0.25, respectively).

CONCLUSIONS

Both methods are equally acceptable in monitoring disease progression in MPM, even though ¹⁸F-FDG PET/CT detected more progression than CT did.

Assessment of immunotherapy response in intracranial malignancy using semi-automatic segmentation on magnetic resonance images

Objective

To explore multi-aspect radiologic assessment of immunotherapy response in intracranial malignancies based on a semi-automatic segmentation technique, and to explore volumetric thresholds with good performance according to RECIST 1.1 thresholds.

Methods

Patients diagnosed with intracranial malignancies and treated with immunotherapy were included retrospectively. In all MR images, target lesions were measured using a semi-automatic segmentation technique that could intelligently generate visual diagrams including RECIST 1.1, total volume, and max. 3D diameter. The changes in parameters were calculated for each patient after immunotherapy. The ROC curve was used to analyze the sensitivity and specificity of the size change of the legion. This was useful to find new volumetric thresholds with better efficiency in response assessment. The changes in total volume were assessed by conventional volumetric thresholds, while RECIST 1.1 thresholds were for the max. 3D diameter. A chi-square test was used to compare the concordance and diagnostic correlation between the response assessment results of the three criteria.

Results

A total of 20 cases (average age, 58 years; range, 23 to 84 years) and 58 follow-up MR examinations after immunotherapy were included in the analysis. The P-value of the chi-square test between RECIST 1.1 and total volume is 0 (P <0.05), same as that in RECIST 1.1 and max. 3D diameter. The kappa value of the former two was 0.775, and the kappa value for the latter two was 0.742. The above results indicate a significant correlation and good concordance for all three criteria. In addition, we also found that the volumetric assessment had the best sensitivity and specificity for the immunotherapy response in intracranial malignancies, with a PR threshold of -64.9% and a PD threshold of 21.4%.

Conclusions

Radiologic assessment of immunotherapy response in intracranial malignancy can be performed by multiple criteria based on semi-automatic segmentation technique on MR images, such as total volume, max. 3D diameter and RECIST 1.1. In addition, new volumetric thresholds with good sensitivity and specificity were found by volumetric assessment.

Quantitative comparison of the efficacies and safety profiles of three first-line non-platinum chemotherapy regimens for advanced non-small cell lung cancer

Objectives: The purpose of this study was to quantify the efficacies and safety profiles of the three first-line non-platinum chemotherapy regimens recommended in the National Comprehensive Cancer Network guidelines.

Materials and Methods: The PubMed and Cochrane Library databases were searched comprehensively, and clinical trials involving patients with advanced non-small cell lung cancer treated with one of three first-line non-platinum regimens (gemcitabine combined with vinorelbine, gemcitabine combined with docetaxel, or gemcitabine alone) were included in the analysis. A parametric proportional hazard survival model was established to analyze the time course of overall survival (OS). The objective response rate (ORR) and incidence rates of grade 3–4 adverse events (AEs) were summarized using a single-arm meta-analysis with a random-effects model.

Results: Seventeen studies met the inclusion criteria. Age and performance status (PS) scores were significant predictors of OS. For each 10-years increase in age, mortality risk increased by 18.5%, and the mortality risk increased by 4% for every 10% increase in the proportion of patients with a PS score of 2. After correcting for the above factors, we found that the three first-line non-platinum chemotherapy regimens did not differ based on OS or toxicity.

Conclusion: There was no significant difference in OS or toxicity among the three first-line non-platinum chemotherapy regimens. Age and PS scores were significant predictors of OS, and their heterogeneity across different studies should be considered in cross-study comparisons and sample size estimations when designing clinical trials.

A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study

Objective

Currently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high‐grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response.

Methods

We conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high‐grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People's Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3–52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X‐ray, CT, contrast‐enhanced magnetic resonance (MR), and positron emission tomography‐computed tomography (PET‐CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure.

Results

For patients without events, a minimum follow‐up of 24 months was achieved. The median follow‐up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression‐free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUV_max) decrease (up to three points), and significant alkaline phosphatase decrease (up to 1 point) were identified as significant predictors of good histological response and constituted the scoring system. A score ≥4 predicts a good response to chemotherapy. The scoring system based on the above factors performed well, achieving an AUC of 0.893. For nonmeasurable lesions (classified by the revised Response Evaluation Criteria in Solid Tumors [RECIST 1.1]), the AUC was 0.901.

Conclusion

We first devised a well‐performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high‐grade bone sarcomas.

Local Investigators Significantly Overestimate Overall Response Rates Compared to Blinded Independent Central Reviews in Uncontrolled Oncology Trials: A Comprehensive Review of the Literature

Several drugs gained market authorization based on the demonstration of improved progression-free survival (PFS), adopted as a primary endpoint in Phase 3 clinical trials. In addition, an increasing number of drugs have been granted accelerated approval, and sometimes regular approval, by the main regulatory agencies based on the evaluation of the overall response rate in Phase 1 and 2 clinical trials. However, while the overall survival is an unbiased measure of drug efficacy, these outcomes rely on the assessment of radiological images and patients’ categorization using standardized response criteria. The evaluation of these outcomes may be influenced by subjective factors, particularly when the analysis is performed locally. In fact, blinding of treatment is not always possible in modern oncology trials. Therefore, a blinded independent central review is often adopted to overcome the problem of expectation bias associated with local investigator assessments. In this regard, we have recently observed that local investigators tend to overestimate the overall response rate in comparison to central reviewers in Phase 2 clinical trials, whereas we did not find any significant evaluation bias between local investigators and central reviews when considering progression-free survival in both Phase 2 and 3 trials. In the present article, we have tried to understand the reasons behind this discrepancy by reviewing the available evidence in the literature. In addition, a further analysis of Phase 2 and 3 clinical trials that included the evaluation of both endpoints showed that local investigators significantly overestimate overall response rates compared to blinded independent central reviews in uncontrolled oncology trials.

iRECIST-based versus non-standardized free text reporting of CT scans for monitoring metastatic renal cell carcinoma: a retrospective comparison

Purpose

Therapy decision for patients with metastatic renal cell carcinoma (mRCC) is highly dependent on disease monitoring based on radiological reports. The purpose of the study was to compare non-standardized, common practice free text reporting (FTR) on disease response with reporting based on response evaluation criteria in solid tumors modified for immune-based therapeutics (iRECIST).

Methods

Fifty patients with advanced mRCC were included in the retrospective, single-center study. CT scans had been evaluated and FTR prepared in accordance with center’s routine practice. For study purposes, reports were re-evaluated using a dedicated computer program that applied iRECIST. Patients were followed up over a period of 22.8 ± 7.9 months in intervals of 2.7 ± 1.8 months.

Weighted kappa statistics was run to assess strength of agreement. Logistic regression was used to identify predictors for different rating.

Results

Agreement between FTR and iRECIST-based reporting was moderate (kappa 0.38 [95% CI 0.2–0.6] to 0.70 [95% CI 0.5–0.9]). Tumor response or progression according to FTR were not confirmed with iRECIST in 19 (38%) or 11 (22%) patients, respectively, in at least one follow-up examination. With FTR, new lesions were frequently not recognized if they were already identified in the recent prior follow-up examination (odds ratio for too favorable rating of disease response compared to iRECIST: 5.4 [95% CI 2.9–10.1].

Conclusions

Moderate agreement between disease response according to FTR or iRECIST in patients with mRCC suggests the need of standardized quantitative radiological assessment in daily clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-03997-0.

Pembrolizumab Alone or with Chemotherapy for Metastatic Non-Small-Cell Lung Cancer: A Systematic Review and Network Meta-Analysis

Pembrolizumab monotherapy has replaced chemotherapy as first-line treatment for patients with metastatic non-small-cell lung cancer with tumor programmed death-ligand 1 expression ≥50%. The benefit of chemotherapy combined with pembrolizumab, as compared to pembrolizumab monotherapy, remains uncertain. This systematic review and network meta-analysis aimed to compare these therapies through a network of randomized controlled trials. Endpoints evaluated were progression-free survival (PFS) and overall survival (OS) expressed as hazard ratio (HR) and restricted mean survival time (RMST) through reconstruction of individual patient data from Kaplan-Meier curves, and objective response rate and adverse events. Four trials were included. Through HR and RMST, combination therapy demonstrated longer PFS and similar OS as compared to pembrolizumab monotherapy. Combination therapy was associated with an increase in response rate and adverse events. Thus, combination therapy can be considered when rapid response or prevention of rapid progression is needed. Further evidence to directly compare these therapies is required.

Validation of Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes

Recently, there has been a growing interest in using real-world data (RWD) to generate real-world evidence that complements clinical trials. To quantify treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are of particular interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured electronic health record (EHR) data and validate these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and tumor registry (TR) data. In addition, we examined and reported data quality issues, especially inconsistencies between the EHR and TR data. Using a survival model, we show that the presence of next treatment was not significantly associated with rwOS, but patients who had longer rwTTNT had longer rwOS, validating the use of rwTTNT as a real-world surrogate marker for measuring cancer endpoints.

Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization

This study aimed to develop a deep learning-based model to simultaneously perform the objective response (OR) and tumor segmentation for hepatocellular carcinoma (HCC) patients who underwent transarterial chemoembolization (TACE) treatment. A total of 248 patients from two hospitals were retrospectively included and divided into the training, internal validation, and external testing cohort. A network consisting of an encoder pathway, a prediction pathway, and a segmentation pathway was developed, and named multi-DL (multi-task deep learning), using contrast-enhanced CT images as input. We compared multi-DL with other deep learning-based OR prediction and tumor segmentation methods to explore the incremental value of introducing the interconnected task into a unified network. Additionally, the clinical model was developed using multivariate logistic regression to predict OR. Results showed that multi-DL could achieve the highest AUC of 0.871 in OR prediction and the highest dice coefficient of 73.6% in tumor segmentation. Furthermore, multi-DL can successfully perform the risk stratification that the low-risk and high-risk patients showed a significant difference in survival (p = 0.006). In conclusion, the proposed method may provide a useful tool for therapeutic regime selection in clinical practice.

The use of external control data for predictions and futility interim analyses in clinical trials

BACKGROUND

External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs).

METHODS

We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs.

RESULTS

Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power.

CONCLUSION

Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

Intracellular Water Lifetime as a Tumor Biomarker to Monitor Doxorubicin Treatment via FFC-Relaxometry in a Breast Cancer Model

This study aims to explore whether the water exchange rate constants in tumor cells can act as a hallmark of pathology status and a reporter of therapeutic outcomes. It has been shown, using 4T1 cell cultures and murine allografts, that an early assessment of the therapeutic effect of doxorubicin can be detected through changes in the cellular water efflux rate constant kio. The latter has been estimated by analyzing the magnetization recovery curve in standard NMR T1 measurements when there is a marked difference in the proton relaxation rate constants (R1) between the intra- and the extra-cellular compartments. In cellular studies, T1 measurements were carried out on a relaxometer working at 0.5 T, and the required difference in R1 between the two compartments was achieved via the addition of a paramagnetic agent into the extracellular compartment. For in-vivo experiments, the large difference in the R1 values of the two-compartments was achieved when the T1 measurements were carried out at low magnetic field strengths. This task was accomplished using a Fast Field Cycling (FFC) relaxometer that was properly modified to host a mouse in its probe head. The decrease in kio upon the administration of doxorubicin is the result of the decreased activity of Na+/K+-ATPase, as shown in an independent test on the cellular uptake of Rb ions. The results reported herein suggest that kio can be considered a non-invasive, early and predictive biomarker for the identification of responsive patients immediately from the first doxorubicin treatment.

Platinum Doublet plus Atezolizumab as First-line Treatment in Metastatic Large Cell Neuroendocrine Carcinoma: A Single Institution Experience

BACKGROUND

Large Cell Neuroendocrine Carcinoma of the Lung (L-LCNEC) is a rare type of neuroendocrine lung cancer that is increasingly diagnosed. However, the optimal management regarding the advanced stage is unclear. The purpose of this article is to present and compare our experience when L-LCNEC is treated as Small Cell Lung Cancer (SCLC).

PATIENTS AND METHODS

Overall, eight cases of L-LCNEC were included. We retrospectively reviewed medical files and reports by accessing the Institution's Data of patients diagnosed with L-LCNEC from April 2019 until December 2020 and evaluated their response to the combination of Platinum - Etoposide - Atezolizumab as first-line chemotherapy.

RESULTS

The overall observed response rate (ORR) of 75%. The median PFS was 6.85 months. The median response duration was 5.5 months.

CONCLUSIONS

Comparing our findings with other retrospective and prospective studies, it seems that the systematic treatment of choice and management in L-LCNEC of the lung should be that of a small cell carcinoma of the lung.

Colorectal Cancer, Liver Metastases and Biotherapies

(1) Background: colorectal cancer (CRC) is one of the deadliest causes of death by cancer worldwide. Its first main metastatic diffusion spreads to the liver. Different mechanisms such as the epithelial-mesenchymal transition and angiogenesis are the characteristics of this invasion. At this stage, different options are possible and still in debate, especially regarding the use of targeted therapeutics and biotherapies. (2) Methods: A review of the literature has been done focusing on the clinical management of liver metastasis of colorectal cancer and the contribution of biotherapies in this field. (3) Results: In a clinical setting, surgeons and oncologists consider liver metastasis in CRC into two groups to launch adapted therapeutics: resectable and non-resectable. Around these two entities, the combination of targeted therapies and biotherapies are of high interest and are currently tested to know in which molecular and clinical conditions they have to be applied to impact positively both on survival and quality of life of patients.

The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

A review of the application of machine learning in molecular imaging

Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

Role in staging and prognostic value of pretherapeutic F-18 FDG PET/CT in patients with gastric MALT lymphoma without high-grade transformation

The purpose of this retrospective study was to investigate the role in staging and prognostic value of pretherapeutic fluorine-18-fluorodeoxyglucose (F-18 FDG) positron emission tomography (PET)/computed tomography (CT) in patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma without high-grade transformation (HT). We retrospectively reviewed 115 consecutive patients with histopathologically confirmed gastric MALT lymphoma without HT who underwent pretherapeutic F-18 FDG PET/CT. Kaplan–Meier and Cox proportional-hazards regression analyses were used to identify independent prognostic factors for disease free survival (DFS) among 13 clinical parameters and three PET parameters. In two of 115 patients (1.7%), the clinical stage appeared higher according to F-18 FDG PET/CT. In univariate analysis, Helicobacter pylori (HP) infection (P = 0.023), treatment modality (P < 0.001), and stage including PET/CT (P = 0.015) were significant prognostic factors for DFS. In multivariate analysis, only treatment modality was an independent prognostic factor (P = 0.003). In conclusion, F-18 FDG PET/CT played an important role in enabling upstaging of patients with gastric MALT lymphoma without HT. F-18 FDG PET/CT may have a prognostic role in gastric MALT lymphoma without HT by contributing to better staging.

Clinical endpoints in oncology - a primer

Clinical endpoints are essential for assessing the safety and efficacy of new cancer therapies. They are used by oncologists to help guide clinical decision making. While overall survival (OS) has frequently been regarded as the "gold standard" primary clinical endpoint, it's utility is constrained by several disadvantages. The time-consuming nature of trials using OS has led to a recent push to explore surrogate clinical endpoints and their potential to serve as primary clinical endpoints in lieu of OS. Additionally, it is becoming evident that other endpoints add valuable information about quality of life and treatment failure as their use is becoming increasingly prevalent in oncology clinical trials. Without a doubt, the use of clinical endpoints will continue to expand and evolve as new cancer therapies are developed and novel treatments, including immunotherapy, draw interest. This review explores the roles of primary and surrogate clinical endpoints as well as the benefits and drawbacks of each specific endpoint. In addition, it directly compares the unique features of each suggesting some of the specific uses each one fulfills.

Characterizing tumor shrinkage as a measure of clinical benefit for immune checkpoint inhibitors

BACKGROUND

We explored whether the effectiveness of immune checkpoint inhibitors (ICIs) can be characterized by incorporating a composite of duration of response (DOR) to complement traditional Response Evaluation Criteria in Solid Tumors (RECIST) criteria for objective response rate (ORR) in an intent-to-treat (ITT) population. Furthermore, the correlation of this novel endpoint, characterized by the restricted mean time in response (RMTR), with overall survival (OS) will be examined.

METHODS

We analyzed ORR alone or in combination with DOR (RMTR) in available phase I, II, and III trials evaluating nivolumab monotherapy or in combination with ipilimumab across solid tumor types. ORR was evaluated per RECIST V.1.1. DOR was estimated using individual patient data in ITT populations regardless of RECIST response, with non-responders imputed as zero. Associations between ORR alone or RMTR and OS were evaluated in the ITT population. DOR curves were generated using the Kaplan-Meier product limit method, and 6-month RMTR, a measure of response durability, was derived from the area under the curves. For ORR and RMTR in the ITT population, the strength of association with OS was analyzed using Pearson correlation coefficients (r).

RESULTS

Nivolumab treatment was associated with longer response durations than active control in responder and ITT populations. Similarly, ORR and RMTR were both significantly correlated with OS (ORR vs OS: r=0.684, p=0.02; RMTR vs OS: r=0.695, p=0.018).

CONCLUSIONS

Combining ORR and DOR (RMTR) to objectively characterize tumor shrinkage in an ITT patient population is a novel approach that appears to correlate well with OS in patients treated with nivolumab monotherapy or in combination with ipilimumab. This endpoint may provide a more complete characterization of tumor shrinkage to incorporate into the design of future ICI clinical trials. However, confirmation of this approach will require further research.

Trends in Phase II Trials for Cancer Therapies

Background: Drug combinations are the standard of care in cancer treatment. Identifying effective cancer drug combinations has become more challenging because of the increasing number of drugs. However, a substantial number of cancer drugs stumble at Phase III clinical trials despite exhibiting favourable efficacy in the earlier Phase. Methods: We analysed recent Phase II cancer trials comprising 2165 response rates to uncover trends in cancer therapies and used a null model of non-interacting agents to infer synergistic and antagonistic drug combinations. We compared our latest efficacy dataset with a previous dataset to assess the progress of cancer therapy. Results: Targeted therapies reach higher response rates when used in combination with cytotoxic drugs. We identify four synergistic and 10 antagonistic combinations based on the observed and expected response rates. We demonstrate that recent targeted agents have not significantly increased the response rates. Conclusions: We conclude that either we are not making progress or response rate measured by tumour shrinkage is not a reliable surrogate endpoint for the targeted agents.

Local Investigators Significantly Overestimate Overall Response Rates Compared to Blinded Independent Central Reviews in Phase 2 Oncology Trials

The overall response rate (ORR) is a largely adopted outcome measure in early-phase oncology trials. ORR is highly relevant in cancer drug development at the time of deciding whether to move to confirmatory phase 3 trials; moreover, ORR is gaining increasing relevance in fast-track registration procedures. No systematic analysis has been conducted so far to investigate whether a discrepancy exists between ORR assessed by local investigators and those assessed by blinded reviewers in phase 2 oncology trials. In this study, we carried out a search in the clinicaltrials.gov and EudraCT databases, looking at the trials reporting the results of both investigator-assessed and independently-assessed ORR. A discrepancy index was obtained by calculating the ratio of each investigator-assessed ORR on the corresponding independently assessed ORR, so that a discrepancy index >1 indicates that the investigator was "more optimistic," whereas a discrepancy index <1 indicates the opposite. We also analyzed different subgroups (by tumor type, by drug type, by year). Twenty trials met the search criteria; in some cases, >1 comparison was conducted in the trial, so that the total number of comparisons analyzed was 33. The estimated mean discrepancy index was 1.175 (95% confidence interval, 1.083-1.264; n = 33). In conclusion, local investigators significantly overestimate ORR compared to paired blinded reviewers in phase 2 oncology trials. This may represent a risk in drug development, when deciding whether to move to confirmatory, more expensive phase 3 trials. Blinded independent central review should be used in ORR assessment if a more conservative estimate of treatment efficacy is required, as in the case of fast-track drug developments leading to accelerated approvals of cancer therapies.

Strategies for Testing Intervention Matching Schemes in Cancer

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as "intervention matching schemes" (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, such as basket, umbrella, and adaptive trials. In addition, IMS are at the heart of proposed "rapid learning systems" in hospitals, and implicit in cell replacement strategies, such as cytotoxic T-cell therapies targeting patient-specific neo-antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.

Positron Emission Tomography-Based Response to Target and Immunotherapies in Oncology

2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) is a promising tool to support the evaluation of response to either target therapies or immunotherapy with immune checkpoint inhibitors both in clinical trials and, in selected patients, at the single patient's level. The present review aims to discuss available evidence related to the use of [¹⁸F]FDG PET (Positron Emission Tomography) to evaluate the response to target therapies and immune checkpoint inhibitors. Criteria proposed for the standardization of the definition of the PET-based response and complementary value with respect to morphological imaging are commented on. The use of PET-based assessment of the response through metabolic pathways other than glucose metabolism is also relevant in the framework of personalized cancer treatment. A brief discussion of the preliminary evidence for the use of non-FDG PET tracers in the evaluation of the response to new therapies is also provided.