Imaging biomarkers for evaluating tumor response: RECIST and beyond

Multiparametric quantification of T1 and T2 relaxation time of bone metastasis in comparison with red or fatty bone marrow using magnetic resonance fingerprinting

OBJECTIVES

To assess the T1 and T2 values of bone marrow lesions in spine and pelvis derived from magnetic resonance fingerprinting (MRF) and to evaluate the differences in values among bone metastasis, red marrow and fatty marrow.

METHODS

Sixty patients who underwent lumbar spine and pelvic MRI with magnetic resonance fingerprinting were retrospectively included. Among eligible patients, those with bone metastasis, benign red marrow deposition and normal fatty marrow were identified. Two radiologists independently measured the T1 and T2 values from metastatic bone lesions, fatty marrow, and red marrow deposition on three-dimensional-magnetic resonance fingerprinting. Intergroup comparison and interobserver agreement were analyzed.

RESULTS

T1 relaxation time was significantly higher in osteoblastic metastasis than in red marrow (1674.6 ± 436.3 vs 858.7 ± 319.5, p < .001). Intraclass correlation coefficients for T1 and T2 values were 0.96 (p < 0.001) and 0.83 (p < 0.001), respectively. T2 relaxation time of osteoblastic metastasis and red marrow deposition had no evidence of a difference (osteoblastic metastasis, 57.9 ± 25.0 vs red marrow, 58.0 ± 34.4, p = 0.45), as were the average T2 values of osteolytic metastasis and red marrow deposition (osteolytic metastasis, 45.3 ± 15.1 vs red marrow, 58.0 ± 34.4, p = 0.63).

CONCLUSIONS

We report the feasibility of three-dimensional-magnetic resonance fingerprinting based quantification of bone marrow to differentiate bone metastasis from red marrow. Simultaneous T1 and T2 quantification of metastasis and red marrow deposition was possible in spine and pelvis and showed significant different values with excellent inter-reader agreement.

ADVANCE IN KNOWLEDGE

T1 values from three-dimensional-magnetic resonance fingerprinting might be a useful quantifier for evaluating bone marrow lesions.

Navigating the landscape of theranostics in nuclear medicine: current practice and future prospects

Theranostics refers to the combination of diagnostic biomarkers with therapeutic agents that share a specific target expressed by diseased cells and tissues. Nuclear medicine is an exciting component explored for its applicability in theranostic concepts in clinical and research investigations. Nuclear theranostics is based on the employment of radioactive compounds delivering ionizing radiation to diagnose and manage certain diseases employing binding with specifically expressed targets. In the realm of personalized medicine, nuclear theranostics stands as a beacon of potential, potentially revolutionizing disease management. Studies exploring the theranostic profile of radioactive compounds have been presented in this review along with a detailed explanation of radioactive compounds and their theranostic applicability in several diseases. It furnishes insights into their applicability across diverse diseases, elucidating the intricate interplay between these compounds and disease pathologies. Light is shed on the important milestones of nuclear theranostics beginning with radioiodine therapy in thyroid carcinomas, MIBG labelled with iodine in neuroblastoma, and several others. Our perspectives have been put forth regarding the most important theranostic agents along with emerging trends and prospects.

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.

Combined visual and quantitative assessment of somatostatin receptor scintigraphy for staging and restaging of neuroendocrine tumors

PURPOSE

Somatostatin receptor scintigraphy (SRS) using 111In-DTPA-DPhe1-octreotide (pentetreotide) has become an integral part of neuroendocrine neoplasm management. The lack of precise quantification is a disadvantage of SRS. This study aimed to adapt the standardized uptake value (SUV) to SRS, establish the SUV range for physiological uptake in the liver, kidney, and spleen, and elucidate the utility of combined visual and quantitative SRS assessment for staging and restaging of neuroendocrine tumors (NETs).

MATERIALS AND METHODS

This study included 21 patients with NETs who underwent 111In-pentetreotide SRS. The SUV of physiological and pathological uptake was calculated using bone single-photon emission computed tomography (SPECT) quantitative analysis software (GI-BONE). For visual analysis, the primary and metastatic lesions were scored visually on planar and SPECT images using a five-point scale. We assessed the relationships between the SUVs of the liver, kidney, and spleen in the dual phase, and among quantitative indices, visual score, and pathological lesions classification.

RESULTS

Sixty-three NEN lesions were evaluated. The mean ± standard deviation maximum SUVs (SUVmax) were liver: 4 h, 2.6 ± 1.0; 24 h, 2.2 ± 1.0; kidney: 4 h, 8.9 ± 1.8; 24 h, 7.0 ± 2.0; and spleen; 4 h, 11.3 ± 4.5; 24 h, 11.5 ± 7.6. Higher SUVmax was significantly associated with higher visual scores on dual-phase SPECT (4 h, p < 0.001; 24 h, p < 0.001) (4 h: scores 3 and 4, p < 0.05; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01; 24 h: scores 3 and 4, p = 0.0748; scores 3 and 5: p < 0.01; scores 4 and 5: p < 0.01).

CONCLUSION

We adapted the SUV to SRS and established the range of SUV for physiological uptake in the liver, kidney, and spleen. Combined visual and quantitative assessment is useful for imaging individual lesions in greater detail, and may serve as a new tumor marker of SRS for staging and restaging of NETs.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy

OBJECTIVES

This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC).

MATERIALS AND METHODS

A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods.

RESULTS

The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients.

CONCLUSION

The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT.

CLINICAL RELEVANCE STATEMENT

The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment.

KEY POINTS

• The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

A conceptual framework for cautious escalation of anticancer treatment: How to optimize overall benefit and obviate the need for de-escalation trials

BACKGROUND

The developmental workflow of the currently performed phase 1, 2 and 3 cancer trial stages lacks essential information required for the determination of the optimal efficacy threshold of new anticancer regimens. Due to this there is a serious risk of overdosing and/or treating for an unnecessary long time, leading to excess toxicity and a higher financial burden for society. But often post-approval de-escalation trials for dose-optimization and treatment de-intensification are not performed due to failing resources and time. Therefore, the developmental workflow needs to be restructured toward cautious systemic cancer treatment escalation, in order to guarantee optimal efficacy and sustainability.

METHODS

In this manuscript we discuss opportunities to produce the information needed for cautious escalation, based on models of cancer growth and cancer kill kinetics as well as exploratory biomarkers, for the purpose of designing the optimal phase 3 superiority trial. Subsequently, we compare the sample size needed for a phase 3 superiority trial, followed by a necessary de-escalation trial with the sample size needed for a multi-arm phase 3 trial with intervention arms of differing intensity. All essential items are structured within a Framework for Cautious Escalation (FCE). The discussion uses illustrations from the breast cancer setting, but aims to be applicable for all cancers.

RESULTS

The FCE is a promising model of clinical development in oncology to prevent overtreatment and associated issues, especially with regard to the number of repetitive treatment cycles. It will hopefully increase the relevance and success rate of clinical trials, to deliver improved patient-centric outcomes.

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.

Genetic Variants Associated With Response to Platinum-Based Chemotherapy in Non-Small Cell Lung Cancer Patients: A Field Synopsis and Meta-Analysis

Background: Publications on the associations of genetic variants with the response to platinum-based chemotherapy (PBC) in NSCLC patients have surged over the years, but the results have been inconsistent. Here, a comprehensive meta-analysis was conducted to combine eligible studies for a more accurate assessment of the pharmacogenetics of PBC in NSCLC patients. Methods: Relevant publications were searched in PubMed, Scopus, and Web of Science databases through 15 May 2021. Inclusion criteria for eligible publications include studies that reported genotype and allele frequencies of NSCLC patients treated with PBC, delineated by their treatment response (sensitive vs. resistant). Publications on cell lines or animal models, duplicate reports, and non-primary research were excluded. Epidemiological credibility of cumulative evidence was assessed using the Newcastle-Ottawa Scale (NOS) and Venice criteria. Begg's and Egger's tests were used to assess publication bias. Cochran's Q-test and I2 test were used to calculate the odds ratio and heterogeneity value to proceed with the random effects or fixed-effects method. Venice criteria were used to assess the strength of evidence, replication methods and protection against bias in the studies. Results: A total of 121 publications comprising 29,478 subjects were included in this study, and meta-analyses were performed on 184 genetic variants. Twelve genetic variants from 10 candidate genes showed significant associations with PBC response in NSCLC patients with strong or moderate cumulative epidemiological evidence (increased risk: ERCC1 rs3212986, ERCC2 rs1799793, ERCC2 rs1052555, and CYP1A1 rs1048943; decreased risk: GSTM1 rs36631, XRCC1 rs1799782 and rs25487, XRCC3 rs861539, XPC rs77907221, ABCC2 rs717620, ABCG2 rs2231142, and CDA rs1048977). Bioinformatics analysis predicted possible damaging or deleterious effects for XRCC1 rs1799782 and possible low or medium functional impact for CYP1A1 rs1048943. Conclusion: Our results provide an up-to-date summary of the association between genetic variants and response to PBC in NSCLC patients.

Immunotherapy for lung cancer

Immune checkpoint blockers have transformed non-small-cell lung cancer treatment, but they can lead to autoimmune and inflammatory side effects, leading to the concurrent use of immunosuppressive treatments. In this analysis, we delve into the potential of antibodies checkpoint blockade, focusing on CTLA-4 inhibition using ipilimumab, as a groundbreaking cancer immunotherapy. We also concentrate on the role of biomarkers, particularly PD-L1 activity and mutation significance, in predicting the response to programmed cell death protein 1 blockage and the prevalence of side effects associated with immune-related side effects. In describing the patterns of cancer response to immunotherapy, we underline the limitations of response assessment criteria like RECIST and World Health Organization. We also stress the necessity of ongoing studies and clinical trials, standardized guidelines, and additional research to improve response assessment in the era of immunotherapy.

A statistical framework for planning and analysing test-retest studies of repeatability

There is an increasing number of potential quantitative biomarkers that could allow for early assessment of treatment response or disease progression. However, measurements of such biomarkers are subject to random variability. Hence, differences of a biomarker in longitudinal measurements do not necessarily represent real change but might be caused by this random measurement variability. Before utilizing a quantitative biomarker in longitudinal studies, it is therefore essential to assess the measurement repeatability. Measurement repeatability obtained from test-retest studies can be quantified by the repeatability coefficient, which is then used in the subsequent longitudinal study to determine if a measured difference represents real change or is within the range of expected random measurement variability. The quality of the point estimate of the repeatability coefficient, therefore, directly governs the assessment quality of the longitudinal study. Repeatability coefficient estimation accuracy depends on the case number in the test-retest study, but despite its pivotal role, no comprehensive framework for sample size calculation of test-retest studies exists. To address this issue, we have established such a framework, which allows for flexible sample size calculation of test-retest studies, based upon newly introduced criteria concerning assessment quality in the longitudinal study. This also permits retrospective assessment of prior test-retest studies.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND

About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection.

AIM

To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.

METHODS

In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.

RESULTS

Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability.

CONCLUSION

The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Evaluation of Three Imaging Methods to Quantify Key Events in Pelvic Bone Metastasis

BACKGROUND

The purpose of this study is to compare turbo spin echo diffusion-weighted images in radial trajectory (BLADE DWI) with multi-shot echoplanar imaging (RESOLVE DWI) for imaging the metastatic lesion in the pelvic bone to find a correlation between ADC values and standardized uptake values (SUVs) of FDG uptake in PET/CT. The study also seeks to compare the values of metastatic lesions with those of benign bone lesions, specifically red marrow hyperplasia.

METHODS

The retrospective IRB-approved study included patients with bone metastasis and red marrow hyperplasia in the pelvic bone who underwent 3.0 T MRI with BLADE/RESOLVE DWI sequences and F-18 FDG PET/CT within one month. BVC (best value comparator) was used in determining the nature of bone lesions. Apparent diffusion coefficient (ADC) and standardized uptake value (SUV) were measured by a radiologist and a nuclear medicine physician. MRI image quality was graded with a Likert scale regarding the visualization of the sacroiliac joint, sacral neural foramen, hamstring tendon at ischial tuberosity, and tumor border. Signal-to-noise ratio (SNR) and imaging time were compared between the two DWIs. Mean, peak, and maximum SUVs between metastatic and benign red marrow lesions were compared. SUVs and ADC values were compared. AUROC analyses and cut-off values were obtained for each parameter. Mann-Whitney U, Spearman's rho, and Kolmogorov-Smirnov tests were applied using SPSS.

RESULTS

The final study group included 58 bone lesions (19 patients (male: female = 6:13, age 52.5 ± 9.6, forty-four (75.9%) bone metastasis, fourteen (24.1%) benign red marrow hyperplasia). ADCs from BLADE and RESOLVE were significantly higher in bone metastasis than red marrow hyperplasia. BLADE showed higher ADC values, higher anatomical scores, and higher SNR than RESOLVE DWI (p < 0.05). Imaging times were longer for BLADE than RESOLVE (6 min 3 s vs. 3 min 47 s, p < 0.05). There was a poor correlation between ADC values and SUVs (correlation coefficient from 0.04 to 0.31). The AUROC values of BLADE and RESOLVE MRI ranged from 0.892~0.995. Those of PET ranged from 0.877~0.895. The cut-off ADC values between the bone metastasis and red marrow hyperplasia were 355.0, 686.5, 531.0 for BLADE min, max, and average, respectively, and 112.5, 737.0, 273.0 for RESOLVE min, max, and average, respectively. The cut-off SUV values were 1.84, 5.01, and 3.81 for mean, peak, and max values, respectively (p < 0.05).

CONCLUSIONS

Compared with RESOLVE DWI, BLADE DWI showed improved image quality of pelvic bone MRI in the aspect of anatomical depiction and SNR, higher ADC values, albeit longer imaging time. BLADE and RESOLVE could differentiate bone metastasis and red marrow hyperplasia with quantifiable cut-off values. Further study is necessary to evaluate the discrepancy between the quantifiers between PET and MRI.

Whole tumor based texture analysis of magnetic resonance diffusion imaging for colorectal liver metastases: A prospective study for diffusion model comparison and early response biomarker

PURPOSE

To evaluate and compare the diagnostic value of diffusion-related texture analysis parameters obtained from various magnetic resonance diffusion models as early predictors of the clinical response to chemotherapy in patients with colorectal liver metastases (CRLM).

METHODS

Patients (n = 145) with CRLM were prospectively and consecutively enrolled and scanned using diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI)/intravoxel incoherent motion (IVIM)/diffusion kurtosis imaging (DKI) before (baseline) and two-three weeks after (follow-up) commencing chemotherapy. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1). The histogram and texture parameters of each diffusion-related parametric map were analysed between the responding and non-responding groups, screened using LASSO, and fitted with binary logistic regression models. The diagnostic efficacy of each model in the early prediction of CRLM was analysed, and the corresponding receiver operating characteristic (ROC) curve was drawn. The area under the curve (AUC) and 95% confidence intervals (CI) were calculated.

RESULTS

Of the 145 analysed patients, 69 were in the responding group and 76 were in the non-responding group. Among all models, the difference value based on the histogram and texture features of the DKI-derived parameters performed best for the early prediction of CRLM treatment efficacy. The AUC of the DKI model in the validation set reached 0.795 (95% CI 0.652-0.938). Among the IVIM-derived parameters, the difference model based on D and D* performed best, and the AUC in the validation set reached 0.737 (95% CI 0.586-0.889). Finally, in the DWI sequence, the model comprising baseline features performed the best, with an AUC of 0.699 (95% CI 0.537-0.86) in the validation set.

CONCLUSIONS

Baseline DWI parameters and follow-up changes in IVIM and DKI parameters predicted the chemotherapeutic response in patients with CRLM. In addition, as very early predictors, DKI-derived parameters were more effective than DWI- and IVIM-related parameters, in which changes in D-parameters performed best.

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 Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT

BACKGROUND

We aimed to develop a new tumor response assessment method for lung SBRT.

METHODS

In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images.

RESULTS

R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity.

CONCLUSION

The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.

Efficacy Analysis of Neoadjuvant versus Adjuvant Cisplatin-Paclitaxel Regimens for Initial Treatment of FIGO Stages IB3 and IIA2 Cervical Cancer

BACKGROUND Large cancer lesions are often challenging to treat with surgical intervention alone. Neoadjuvant chemotherapy is frequently used for FIGO stage IB3 and IIA2 cervical cancers to optimize the outcomes of radical surgeries. This study aimed to compare the effectiveness of neoadjuvant chemotherapy, followed by adjuvant chemotherapy and radiotherapy, if necessary, with the traditional approach of adjuvant chemotherapy and radiotherapy after radical hysterectomy in treatment-naïve patients with cervical cancer of specified stages. MATERIAL AND METHODS A total of 245 female patients were administered either 70 to 85 mg/m² cisplatin and 165 to 175 mg/m² paclitaxel every 21 days (2 cycles) prior to radical hysterectomy, followed by adjuvant chemotherapy and radiotherapy if needed (neoadjuvant therapy, NT cohort, n=105), or received adjuvant chemotherapy and radiotherapy after radical hysterectomy adjuvant therapy, AT cohort, n=140). RESULTS In the NT cohort, 76% of patients responded to neoadjuvant chemotherapy, while 24% did not. Adverse operative, intraoperative, and postoperative outcomes were significantly more common among the non-responders (P<0.05). After 5 years, 91% of responders and 72% of non-responders survived without recurrence (P=0.0372), and 3% of responders and 28% of non-responders had died (P=0.0005). CONCLUSIONS The resistance to neoadjuvant chemotherapy is a poor prognostic factor. Neoadjuvant chemotherapy followed by radical hysterectomy and adjuvant chemotherapy/radiotherapy appears to be advantageous for cervical cancer patients who respond well to neoadjuvant chemotherapy.

Apatinib added when NSCLC patients get slow progression with EGFR-TKI: A prospective, single-arm study

BACKGROUND

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) acquired resistance was an inevitably events in NSCLC treatment.

AIMS

Intending to overcome the acquired resistance of EGFR-TKI.

MATERIALS & METHODS

A clinical trial was, we enrolled 12 patients who were slowly progressing on first-generation EGFR-TKI, and added apatinib when the patients got slow progression.

RESULTS

Seven patients were included in the efficacy analysis. The median PFS2 of apatinib combined with EGFR-TKI was 8.2 months (95% CI, 7.3 m-NA), and the total PFS reached 20.9 months (95% CI, 17.3 m-NA) when plus PFS1. All the adverse events were manageable. The median PFS was significantly longer for circulating tumor DNA (ctDNA)-cleared patients (8.4 months; 95% CI, 8.2-NA) than for those ctDNA not cleared (7.1 months; 95% CI, 6.9-NA) (p = 0.0082).

DISCUSSION

The addition of apatinib did improve the duration of first-generation EGFR-TKI use, and the duration was better than the first-line use of third-generation EGFR-TKI.

CONCLUSION

The addition of apatinib when the patients got slow progression after initial EGFR-TKI therapy may be a good treatment option and the side effects are controllable. It is possible to monitor treatment efficacy using ctDNA.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI

OBJECTIVES

While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

METHODS

In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation.

RESULTS

Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001.

CONCLUSIONS

Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

KEY POINTS

• A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

Pretreatment diffusion-weighted imaging for prediction of relapsed and refractory primary central nervous system lymphoma

Objectives

A subset of primary central nervous system lymphoma (PCNSL) has been shown to undergo an early relapsed/refractory (R/R) period after first-line chemotherapy. This study investigated the pretreatment clinical and MRI features to predict R/R in PCNSL, emphasizing the apparent diffusion coefficient (ADC) values in diffusion-weighted imaging (DWI).

Methods

This retrospective study investigated the pretreatment MRI features for predicting R/R in PCNSL. Only patients who had undergone complete preoperative and postoperative MRI follow-up studies were included. From January 2006 to December 2021, 52 patients from two medical institutions with a diagnosis of PCNSL were included (median follow-up time, 26.3 months). Among these, 24 (46.2%) had developed R/R (median time to relapse, 13 months). Cox proportional hazard regression analyses were performed to determine hazard ratios for all parameters.

Results

Significant predictors of R/R in PCNSL were female sex, complete response (CR) to first-line chemotherapy, and ADC value/ratio (p < 0.05). Cut-off points of ADC values and ADC ratios for prediction of R/R were 0.68 × 10-3 mm2/s and 0.97, with AUCs of 0.78 and 0.77, respectively (p < 0.05). Multivariate Cox proportional hazards analysis showed that failure of CR to first-line chemotherapy and low ADC values (<0.68 × 10-3 mm2/s) were significant risk factors for R/R, with hazard ratios of 5.22 and 14.45, respectively (p < 0.05). Kaplan-Meier analysis showed that lower ADC values and ratios predicted significantly shorter progression-free survival (p < 0.05).

Conclusion

Pretreatment ADC values in DWI offer quantitative valuable information for the treatment planning in PCNSL.

Recent Advances in Image-Guided Locoregional Therapies for Primary Liver Tumors

Primary liver cancer is the leading cause of cancer-related deaths worldwide. with incidences predicted to rise over the next several decades. Locoregional therapies, such as radiofrequency or microwave ablation, are described as image-guided percutaneous procedures, which offer either a curative intent for early-stage hepatocellular carcinoma or bridging/downstaging for surgical resection or transplantation. Catheter-driven locoregional therapies, such as transarterial chemoembolization and radioembolization, induce tumor hypoxia, can be palliative, and improve survival for early-to-intermediate hepatocellular carcinoma and unresectable intrahepatic cholangiocarcinoma. Herein, we provide a comprehensive overview of the antineoplastic mechanisms underpinning locoregional therapies, different treatment approaches, and the current state of the literature for the efficacy of locoregional therapies for primary liver cancer. We also discuss emerging advancements, such as the adjuvant use of immunotherapies and molecular targeting agents with locoregional therapy, for the treatment of primary liver cancer.

Immune checkpoint inhibition: a future guided by radiology

The limitation of the function of antitumour immune cells is a common hallmark of cancers that enables their survival. As such, the potential of immune checkpoint inhibition (ICI) acts as a paradigm shift in the treatment of a range of cancers but has not yet been fully capitalised. Combining minimally and non-invasive locoregional therapies offered by radiologists with ICI is now an active field of research with the aim of furthering therapeutic capabilities in medical oncology. In parallel to this impending advancement, the "imaging toolbox" available to radiologists is also growing, enabling more refined tumour characterisation as well as greater accuracy in evaluating responses to therapy. Options range from metabolite labelling to cellular localisation to immune checkpoint screening. It is foreseeable that these novel imaging techniques will be integrated into personalised treatment algorithms. This growth in the field must include updating the current standardised imaging criteria to ensure they are fit for purpose. Such criteria is crucial to both appropriately guide clinical decision-making regarding next steps of treatment, but also provide reliable prognosis. Quantitative approaches to these novel imaging techniques are also already being investigated to further optimise personalised therapeutic decision-making. The therapeutic potential of specific ICIs and locoregional therapies could be determined before administration thus limiting unnecessary side-effects whilst maintaining efficacy. Several radiological aspects of oncological care are advancing simultaneously. Therefore, it is essential that each development is assessed for clinical use and optimised to ensure the best treatment decisions are being offered to the patient. In this review, we discuss state of the art advances in novel functional imaging techniques in the field of immuno-oncology both pre-clinically and clinically.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

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 entropy and fractal dimension as biomarkers for tumor growth and treatment response using cellular automata

Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study cancer therapies' effects, which are often designed to disrupt single-cell dynamics. In this work, we propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which a time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination. At the same time, entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the prognostic value of the proposed biomarkers could vary considerably with time. Thus, it is essential to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells scattered along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

Purpose

The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).

Methods

The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.

Results

There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p&lt;0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p&lt;0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p&lt;0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p&lt;0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).

Conclusion

Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.

Summary

While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p&lt;0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p&lt;0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p&lt;0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p&lt;0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.

Prospective of 31 P MR Spectroscopy in Hepatopancreatobiliary Cancer: A Systematic Review of the Literature

BACKGROUND

The incidence of liver and pancreatic cancer is rising. Patients benefit from current treatments, but there are limitations in the evaluation of (early) response to treatment. Tumor metabolic alterations can be measured noninvasively with phosphorus (³¹ P) magnetic resonance spectroscopy (MRS).

PURPOSE

To conduct a quantitative analysis of the available literature on ³¹ P MRS performed in hepatopancreatobiliary cancer and to provide insight into its current and potential for therapy (non-) response assessment.

POPULATION

Patients with hepatopancreatobiliary cancer. FIELD STRENGTH/SEQUENCE: ³¹ P MRS.

ASSESSMENT

The PubMed, EMBASE, and Cochrane library databases were systematically searched for studies published to 17 March 17, 2022. All ³¹ P MRS studies in hepatopancreatobiliary cancer reporting ³¹ P metabolite levels were included.

STATISTICAL TESTS

Relative differences in ³¹ P metabolite levels/ratios between patients before therapy and healthy controls, and the relative changes in ³¹ P metabolite levels/ratios in patients before and after therapy were determined.

RESULTS

The search yielded 10 studies, comprising 301 subjects, of whom 132 (44%) healthy volunteers and 169 (56%) patients with liver cancer of various etiology. To date, ³¹ P MRS has not been applied in pancreatic cancer. In liver cancer, alterations in levels of ³¹ P metabolites involved in cell proliferation (phosphomonoesters [PMEs] and phosphodiesters [PDEs]) and energy metabolism (ATP and inorganic phosphate [Pi]) were observed. In particular, liver tumors were associated with elevations of PME/PDE and PME/Pi compared to healthy liver tissue, although there was a broad variety among studies (elevations of 2%-267% and 21%-233%, respectively). Changes in PME/PDE in liver tumors upon therapy were substantial, yet very heterogeneous and both decreases and increases were observed, whereas PME/Pi was consistently decreased after therapy in all studies (-13% to -76%).

DATA CONCLUSION

³¹ P MRS has great potential for treatment monitoring in oncology. Future studies are needed to correlate the changes in ³¹ P metabolite levels in hepatopancreatobiliary tumors with treatment response.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.

The Role of ERα and ERβ in Castration-Resistant Prostate Cancer and Current Therapeutic Approaches

Castration-resistant prostate cancer, or CRPC, is an aggressive stage of prostate cancer (PCa) in which PCa cells invade nearby or other parts of the body. When a patient with PCa goes through androgen deprivation therapy (ADT) and the cancer comes back or worsens, this is called CRPC. Instead of androgen-dependent signalling, recent studies show the involvement of the estrogen pathway through the regulation of estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) in CRPC development. Reduced levels of testosterone due to ADT lead to low ERβ functionality in inhibiting the proliferation of PCa cells. Additionally, ERα, which possesses androgen independence, continues to promote the proliferation of PCa cells. The functions of ERα and ERβ in controlling PCa progression have been studied, but further research is needed to elucidate their roles in promoting CRPC. Finding new ways to treat the disease and stop it from becoming worse will require a clear understanding of the molecular processes that can lead to CRPC. The current review summarizes the underlying processes involving ERα and ERβ in developing CRPC, including castration-resistant mechanisms after ADT and available medication modification in mitigating CRPC progression, with the goal of directing future research and treatment.

CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study

BACKGROUND

Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment.

PURPOSE

To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment.

MATERIAL AND METHODS

A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model.

RESULTS

In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model.

CONCLUSION

Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.

Head-to-head comparisons of [68Ga]Ga-PSMA-11 PET/CT, multiparametric MRI, and prostate-specific antigen for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer patients: a prospective study

PURPOSE

The optimal tool to evaluate the tumour therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa) remains uncertain. We compared the role of [⁶⁸Ga]-labeled prostate-specific membrane antigen (PSMA)-11 positron emission tomography/computerized tomography ([⁶⁸Ga]Ga-PSMA-11 PET/CT), multiparametric MRI (mpMRI), and prostate-specific antigen (PSA) and assessed the practical value of the recent European Association of Urology and European Association of Nuclear Medicine (EAU/EANM) recommended criteria of PSMA PET/CT to evaluate the therapeutic responses to NCHT in patients with high-risk non-metastatic PCa.

METHODS

This prospective study included 72 high-risk non-metastatic PCa patients receiving NCHT followed by radical prostatectomy from June 2021 to March 2022. PSA testing, [⁶⁸Ga]Ga-PSMA-11 PET/CT, and mpMRI scanning were conducted in all patients before and after NCHT. Therapeutic responses to NCHT were evaluated with PSA, RECIST 1.1, PERCIST 1.0, and EAU/EANM recommended criteria. Postoperative pathological results were considered the reference standard. A favourable pathological response was defined as pathologic complete remission (pCR) or minimal residual disease (MRD). Diagnostic accuracy was assessed by sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV), negative predictive value (NPV), and Cohen's kappa index. Logistic regression analysis was used to determine the independent predictive value of [⁶⁸Ga]Ga-PSMA-11 PET/CT-derived parameters.

RESULTS

All cases experienced a marked decrease in PSA levels after NCHT. Twenty-four (33.33%) cases experienced a favourable pathological response, including five (6.94%) cases of pCR and 19 (26.39%) cases of MRD. According to the results of [⁶⁸Ga]Ga-PSMA-11 PET/CT, EAU/EANM recommended criteria indicated that 20 (27.78%) cases had a CR, whereas PERCIST 1.0 criteria indicated that 23 (31.94%) cases had a CR. There was a strong association between EAU/EANM recommended criteria and PERCIST 1.0 criteria (Pearson's R=0.857). The sensitivity (75.00%, 79.17% vs. 58.33%, 58.33%), specificity (95.83%, 91.67% vs. 83.33%, 68.75%), PLR (18.00, 9.50 vs. 3.50, 1.87), NLR (0.26, 0.23 vs. 0.50, 0.61), PPV (90.0%, 82.6% vs. 63.6%, 48.3%), and NPV (88.5%, 89.8% vs. 80.0%, 76.7%) of [⁶⁸Ga]Ga-PSMA-11 PET/CT (including EAU/EANM recommended criteria and PERCIST 1.0 criteria) to predict favourable pathological responses were all superior to those of mpMRI and nadir PSA. The kappa index to predict a favourable pathological response was 0.257 for PSA, 0.426 for RECIST 1.1, 0.716 for PERCIST 1.0, and 0.739 for EAU/EANM recommended criteria. Multivariate logistic analysis revealed that the post-NCHT maximum standardized uptake value (SUVmax) before radical prostatectomy was an independent predictor of a favourable pathological response to NCHT.

CONCLUSIONS

[⁶⁸Ga]Ga-PSMA-11 PET/CT had a better concordance with a favourable pathological response to NCHT compared with nadir PSA and mpMRI. EAU/EANM recommended criteria and PERCIST 1.0 criteria performed equally to identify pathological responders when [⁶⁸Ga]Ga-PSMA-11 PET/CT was used as a therapeutic response assessment tool.

Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer

AIM

To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively.

MATERIAL AND METHODS

EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result.

RESULTS

Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram.

CONCLUSION

The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.

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.

Changes in Oncogene Expression in Experimental Glioblastoma 101.8 Rats during Therapy with PLGA Nanoparticles Loaded with Doxorubicin

We compared the expression of the main glioblastoma oncogenes during therapy with doxorubicin (Dox) and Dox in nanoparticles based on a copolymer of lactic and glycolic acids (Dox-PLGA) at a delayed start of treatment. Late initiation of Dox-PLGA therapy of glioblastoma showed an increase in the expression of multiple drug resistance genes, such as Abcb1b and Mgmt, and a decrease in Sox2 expression. Increased expression of other oncogenes (Melk, Wnt3, Gdnf, and Pdgfra) were observed during both Dox and Dox-PLGA therapy. These changes indicate increased tumor aggressiveness and its resistance to cytostatics at the late start of therapy.

Case report and literature review: PET/CT in the evaluation of response to treatment of liver metastasis from colorectal cancer with DEBIRI-TACE

Background

Irinotecan-loaded drug-eluting beads transarterial chemoembolization (DEBIRI-TACE) is a safe and effective therapeutic option for unresectable colorectal liver metastases (CRLM). The evaluation of treatment response after DEBIRI-TACE is very important for assessing the patient's condition. At present, the Response Evaluation Criteria in Solid Tumors (RECIST) with the tumor size obtained by CT and/or MRI and PET Response Criteria in Solid Tumors (PERCIST) based on fluorodeoxyglucose-positron emission tomography/computed tomography (FDG PET/CT) are used for evaluating the response to therapy of solid tumors; however, their value in the assessment of treatment response after DEBIRI-TACE remains unclear.

Case presentation

A 52-year-old male with unresectable simultaneous CRLM was treated in the Affiliated Hospital of Yanbian University with DEBIRI-TACE combined with systemic chemotherapy and targeted therapy. Carcinoembryonic antigen levels decreased by 82.50% after 27 days of treatment. At 6 weeks post-surgery, FDG-PET/CT showed that the maximum standardized uptake value (SUVmax) of intrahepatic lesions was reduced to 62.14%. Abdominal MRI revealed that the sum of target lesion diameters was less than 30% that at baseline. PERCIST indicated partial metabolic response, whereas RECIST suggested stable disease.

Conclusion

FDG PET/CT-based PERCIST may be accurate in determining treatment response and evaluating patient prognosis after DEBIRI-TACE in unresectable CRLM.

Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer

OBJECTIVE

To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).

METHODS

106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.

RESULTS

LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).

CONCLUSION

The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

Prospective Investigation of 18FDG-PET/MRI with Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Survival in Patients with Oropharyngeal or Hypopharyngeal Carcinoma

To prospectively investigate the prognostic value of 18F-FDG PET/MRI in patients with oropharyngeal or hypopharyngeal squamous cell carcinomas (OHSCC) treated by chemoradiotherapy. The study cohort consisted of patients with OHSCC who had undergone integrated PET/MRI prior to chemoradiotherapy or radiotherapy. Imaging parameters derived from intravoxel incoherent motion (IVIM), dynamic contrast-enhanced MRI (DCE-MRI), and 18F-FDG PET were analyzed in relation to overall survival (OS) and recurrence-free survival (RFS). In multivariable analysis, T classification (p < 0.001), metabolic tumor volume (p = 0.013), and pseudo-diffusion coefficient (p = 0.008) were identified as independent risk factors for OS. The volume transfer rate constant (p = 0.015), initial area under the curve (p = 0.043), T classification (p = 0.018), and N classification (p = 0.018) were significant predictors for RFS. The Harrell’s c-indices of OS and RFS obtained from prognostic models incorporating clinical and PET/MRI predictors were significantly higher than those derived from the traditional TNM staging system (p = 0.001). The combination of clinical risk factors with functional parameters derived from IVIM and DCE-MRI plus metabolic PET parameters derived from 18F-FDG PET in integrated PET/MRI outperformed the information provided by traditional TNM staging in predicting the survival of patients with OHSCC.

Transwell-Hypoxia Method Facilitates the Outgrowth of 3D-Printed Collagen Scaffolds Loaded with Cryopreserved Patient-Derived Melanoma Explants

A previous study from our laboratory demonstrated the effects of in vitro three-dimensional (3D)-printed collagen scaffolds on the maintenance of cryopreserved patient-derived melanoma explants (PDMEs). However, it remains unknown whether 3D-printed collagen scaffolds (3D-PCSs) can be harmonized with any external culture conditions to increase the growth of cryopreserved PDMEs. In this study, 3D-PCSs were manufactured with a 3DX bioprinter. The 3D-printed collagen scaffold-on-frame construction was loaded with fragments of cryopreserved PDMEs (approximately 1-2 mm). 3D-PCSs loaded with patient-derived melanoma explants (3D-PCS-PDMEs) were incubated using two types of methods: (1) in transwells in the presence of a low concentration of oxygen (transwell-hypoxia method) and (2) using a traditional adherent attached to the bottom flat surface of a standard culture dish (traditional flat condition). In addition, we used six different types of media (DMEM high glucose, MEM α, DMEM/F12, RPMI1640, fibroblast basal medium (FBM), and SBM (stem cell basal medium)) for 7 days. The results reveal that the culture conditions of MEM α, DMEM/F12, and FBM using the transwell-hypoxia method show greater synergic effects on the outgrowth of the 3D-PCS-PDME compared to the traditional flat condition. In addition, the transwell-hypoxia method shows a higher expression of the MMP14 gene and the multidrug-resistant gene product 1 (MDR1) than in the typical culture method. Taken together, our findings suggest that the transwell-hypoxia method could serve as an improved, 3D alternative to animal-free testing that better mimics the skin's microenvironment using in vitro PDMEs.

Radiomics in neuro-oncological clinical trials

The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the use of statistical methods in research, and the development of codes of ethics, have considerably influenced the way clinical trials are conducted today. In addition, methods from the broad field of artificial intelligence, such as radiomics, have the potential to considerably affect clinical trials and clinical practice in the future. Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis. In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related changes from tumour progression. Despite promising results, radiomics is not yet established in routine clinical practice nor in clinical trials. In this Viewpoint, the European Organization for Research and Treatment of Cancer Brain Tumour Group summarises the current status of radiomics, discusses its potential and limitations, envisions its future role in clinical trials in neuro-oncology, and provides guidance on how to address the challenges in radiomics.

Treatment Assessment of pNET and NELM after Everolimus by Quantitative MRI Parameters

Assessment of treatment response to targeted therapies such as everolimus is difficult, especially in slow-growing tumors such as NETs. In this retrospective study, 17 patients with pancreatic neuroendocrine tumors (pNETs) and hepatic metastases (NELMs) (42 target lesions) who received everolimus were analyzed. Intralesional signal intensities (SI) of non-contrast T1w, T2w and DCE imaging, and apparent diffusion coefficients (ADCmean and ADCmin) of DWI, were measured on baseline and first follow-up MRI after everolimus initiation. Response assessment was categorized according to progression-free survival (PFS), with responders (R) showing a PFS of ≥11 months. ADCmin of NELMs decreased in Rs whereas it increased in non-responders (NR). Percentual changes of ADCmin and ADCmean differed significantly between response groups (p < 0.03). By contrast, ADC of the pNETs tended to increase in Rs, while there was no change in NRs. Tumor-to-liver (T/L) ratio of T1 SI of NELMs increased in Rs and decreased in NRs, and percentual changes differed significantly between response groups (p < 0.02). T1 SI of the pNETs tended to decrease in Rs and increase in Ns. The quotient of pretherapeutic and posttherapeutic ADCmin values (DADCmin) and length of everolimus treatment showed significant association with PFS in univariable Cox analysis. In conclusion, quantitative MRI, especially DWI, seems to allow treatment assessment of pNETs with NELMs under everolimus. Interestingly, the responding NELMs showed decreasing ADC values, and there might be an opposite effect on ADC and T1 SI between NELMs and pNETs.

Individualized Mini-Panel Sequencing of ctDNA Allows Tumor Monitoring in Complex Karyotype Sarcomas

Soft tissue sarcomas (STS) are rare tumors of mesenchymal origin with high mortality. After curative resection, about one third of patients suffer from distant metastases. Tumor follow-up only covers a portion of recurrences and is associated with high cost and radiation burden. For metastasized STS, only limited inferences can be drawn from imaging data regarding therapy response. To date there are no established and evidence-based diagnostic biomarkers for STS due to their rarity and diversity. In a proof-of-concept study, circulating tumor DNA (ctDNA) was quantified in (n = 25) plasma samples obtained from (n = 3) patients with complex karyotype STS collected over three years. Genotyping of tumor tissue was performed by exome sequencing. Patient-individual mini-panels for targeted next-generation sequencing were designed encompassing up to 30 mutated regions of interest. Circulating free DNA (cfDNA) was purified from plasma and ctDNA quantified therein. ctDNA values were correlated with clinical parameters. ctDNA concentrations correlated with the tumor burden. In case of full remission, no ctDNA was detectable. Patients with a recurrence at a later stage showed low levels of ctDNA during clinical remission, indicating minimal residual disease. In active disease (primary tumor or metastatic disease), ctDNA was highly elevated. We observed direct response to treatment, with a ctDNA decline after tumor resections, radiotherapy, and chemotherapy. Quantification of ctDNA allows for the early detection of recurrence or metastases and can be used to monitor treatment response in STS. Therapeutic decisions can be made earlier, such as the continuation of a targeted adjuvant therapy or the implementation of extended imaging to detect recurrences. In metastatic disease, therapy can be adjusted promptly in case of no response. These advantages may lead to a survival benefit for patients in the future.

Advances in Cancer Diagnosis: Bio-Electrochemical and Biophysical Characterizations of Cancer Cells

Cancer is a worldwide leading cause of death, and it is projected that newly diagnosed cases globally will reach 27.5 million each year by 2040. Cancers (malignant tumors), unlike benign tumors are characterized by structural and functional dedifferentiation (anaplasia), breaching of the basement membrane, spreading to adjacent tissues (invasiveness), and the capability to spread to distant sites (metastasis). In the cancer biology research field, understanding and characterizing cancer metastasis as well as features of cell death (apoptosis) is considered a technically challenging subject of study and clinically is very critical and necessary. Therefore, in addition to the cytochemical methods traditionally used, novel biophysical and bioelectrochemical techniques (e.g., cyclic voltammetry and electrochemical impedance spectroscopy), atomic force microscopy, and electron microscopic methods are increasingly being deployed to better understand these processes. Implementing those methods at the preclinical level enables the rapid screening of new anticancer drugs with understanding of their central mechanism for cancer therapy. In this review, principles and basic concepts of new techniques suggested for metastasis, and apoptosis examinations for research purposes are introduced, along with examples of each technique. From our recommendations, the privilege of combining the bio-electrochemical and biosensing techniques with the conventional cytochemical methods either for research or for biomedical diagnosis should be emphasized.

Response Evaluation Following Radiation Therapy With 18F-FDG PET/CT: Common Variants of Radiation-Induced Changes and Potential Pitfalls

Radiation therapy (RT) is one of the cornerstones in cancer treatment and approximately half of all patients will receive some form of RT during the course of their cancer management. Response evaluation after RT and follow-up imaging with ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) can be complicated by RT-induced acute, chronic or consequential effects. There is a general consensus that ¹⁸F-FDG PET/CT for response evaluation should be delayed for 12 weeks after completing RT to minimize the risk of false-positive findings. Radiation-induced late side effects in normal tissue can take years to develop and eventually cause symptoms that on imaging can potentially mimic recurrent disease. Imaging findings in radiation induced injuries depend on the normal tissue included in the irradiated volume and the radiation therapy regime including the total dose delivered, dose per fraction and treatment schedule. The intent for radiation therapy should be taken in consideration when evaluating the response on imaging, that is palliative vs curative or neoadjuvant vs adjuvant RT. Imaging findings can further be distorted by altered anatomy and sequelae following surgery within the radiation field. An awareness of common PET/CT-induced changes/injuries is essential when interpreting ¹⁸F-FDG PET/CT as well as obtaining a complete medical history, as patients are occasionally scanned for an unrelated cause to previously RT treated malignancy. In addition, secondary malignancies due to carcinogenic effects of radiation exposure in long-term cancer survivors should not be overlooked. ¹⁸F-FDG PET/CT can be very useful in response evaluation and follow-up in patients treated with RT, however, variants and pitfalls are common and it is important to remember that radiation-induced injury is often a diagnosis of exclusion.

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Simple Summary

Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications.

Abstract

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

SNMMI Clinical Trials Network Research Series for Technologists: Clinical Research Primer- Use of Imaging Agents in Therapeutic Drug Development and Approval

The process of bringing a new drug to market is complex and has recently necessitated a new drug discovery paradigm for the pharmaceutical industry that is both more efficient and more economical. Key to this has been the increasing use of nuclear medicine and molecular imaging to support drug discovery efforts by answering critical questions on the pathway for development and approval of a new therapeutic drug. Some of these questions include: (i) Does the new drug reach its intended target in the body at sufficient levels to effectively treat or diagnose disease without unacceptable toxicity? (ii) How is the drug absorbed, metabolized, and excreted? (iii) What is the effective dose in humans? To conduct the appropriate imaging studies to answer such questions, pharmaceutical companies are increasingly partnering with molecular imaging departments. Nuclear medicine technologists are critical to this process as they perform scans to collect the qualitative and quantitative imaging data used to measure study endpoints. This article describes preclinical and clinical research trials and provides an overview of the different ways that radiopharmaceuticals are used to answer critical questions during therapeutic drug development.

Immuno-Imaging (PET/SPECT)-Quo Vadis?

The use of immunotherapy has revolutionized the treatment regimen of certain cancer types, but response assessment has become a difficult task with conventional methods such as CT/MRT or FDG PET-CT and the classical response criteria such as RECIST or PERCIST which have been developed for chemotherapeutic treatment. Plenty of new tracers have been published to improve the assessment of treatment response and to stratify the patient population. We gathered the information on published tracers (in total, 106 individual SPECT/PET tracers were identified) and performed a descriptor-based analysis; in this way, we classify the tracers with regard to target choice, developability (probability to progress from preclinical stage into the clinic), translatability (probability to be widely applied in the 'real world'), and (assumed) diagnostic quality. In our analysis, we show that most tracers are targeting PD-L1, PD-1, CTLA-4, and CD8 receptors by using antibodies or their fragments. Another finding is that plenty of tracers possess only minor iterations regarding chelators and nuclides instead of approaching the problem in a new innovative way. Based on the data, we suggest an orthogonal approach by targeting intracellular targets with PET-activatable small molecules that are currently underrepresented.

Early Assessment of Chemotherapy Response in Advanced Non-Small Cell Lung Cancer with Circulating Tumor DNA

Monitoring treatment efficacy early during therapy could enable a change in treatment to improve patient outcomes. We report an early assessment of response to treatment in advanced NSCLC using a plasma-only strategy to measure changes in ctDNA levels after one cycle of chemotherapy. Plasma samples were collected from 92 patients with Stage IIIB-IV NSCLC treated with first-line chemo- or chemoradiation therapies in an observational, prospective study. Retrospective ctDNA analysis was performed using next-generation sequencing with a targeted 198-kb panel designed for lung cancer surveillance and monitoring. We assessed whether changes in ctDNA levels after one or two cycles of treatment were associated with clinical outcomes. Subjects with ≤50% decrease in ctDNA level after one cycle of chemotherapy had a lower 6-month progression-free survival rate (33% vs. 58%, HR 2.3, 95% CI 1.2 to 4.2, log-rank p = 0.009) and a lower 12-month overall survival rate (25% vs. 70%, HR 4.3, 95% CI 2.2 to 9.7, log-rank p < 0.001). Subjects with ≤50% decrease in ctDNA level after two cycles of chemotherapy also had shorter survival. Using non-invasive liquid biopsies to measure early changes in ctDNA levels in response to chemotherapy may help identify non-responders before standard-of-care imaging in advanced NSCLC.

A Review of Imaging Methods to Assess Ultrasound-Mediated Ablation

Ultrasound ablation techniques are minimally invasive alternatives to surgical resection and have rapidly increased in use. The response of tissue to HIFU ablation differs based on the relative contributions of thermal and mechanical effects, which can be varied to achieve optimal ablation parameters for a given tissue type and location. In tumor ablation, similar to surgical resection, it is desirable to include a safety margin of ablated tissue around the entirety of the tumor. A factor in optimizing ablative techniques is minimizing the recurrence rate, which can be due to incomplete ablation of the target tissue. Further, combining focal ablation with immunotherapy is likely to be key for effective treatment of metastatic cancer, and therefore characterizing the impact of ablation on the tumor microenvironment will be important. Thus, visualization and quantification of the extent of ablation is an integral component of ablative procedures. The aim of this review article is to describe the radiological findings after ultrasound ablation across multiple imaging modalities. This review presents readers with a general overview of the current and emerging imaging methods to assess the efficacy of ultrasound ablative treatments.