Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy

Integration of Circulating Tumor DNA and Metabolic Parameters on 18F-Fludeoxyglucose Positron Emission Tomography for Outcome Prediction in Unresectable Locally Advanced Non-Small Cell Lung Cancer

This prospective study explores the prognostic value of circulating tumor DNA (ctDNA) and positron emission tomography/computed tomograpy (PET/CT) in unresectable locally advanced non-small cell lung cancer (LA-NSCLC) treated with definitive chemoradiotherapy (CRT). The discovery set includes 62 patients, with 62 baseline and 53 post-CRT plasma samples. PET/CT is performed at baseline, and 33 patients undergo mid-treatment scans after 40 Gy. Baseline ctDNA is detected in 71.0% of patients. Pre-treatment ctDNA concentration correlates with total metabolic tumor volume (TMTV) (p < 0.001) and total lesion glycolysis (TLG) (p = 0.001) but not treatment response or survival. However, patients with undetectable ctDNA and low TMTV show significantly longer progression-free survival (PFS) (34.2 vs 10.1 months, p = 0.027). Post-CRT, ctDNA is detected in 47.2% of patients, while ctDNA concentration (p = 0.005) and variant allele frequency (VAF) (p = 0.005) significantly decline. Undetectable post-CRT ctDNA associates with longer PFS (p < 0.001) and overall survival (OS) (p = 0.001). Higher ∆TMTV correlates with improved PFS and OS. Similar findings were obtained in a test of 19 patients. These results highlight post-CRT ctDNA and ∆TMTV as robust prognostic markers, potentially identifying patients who may forgo ICI consolidation.

Computed tomography-based delta-radiomics analysis for preoperative prediction of ISUP pathological nuclear grading in clear cell renal cell carcinoma

BACKGROUND

Nuclear grading of clear cell renal cell carcinoma (ccRCC) plays a crucial role in diagnosing and managing the disease.

OBJECTIVE

To develop and validate a CT-based Delta-Radiomics model for preoperative assessment of nuclear grading in renal clear cell carcinoma.

MATERIALS AND METHODS

This retrospective analysis included surgical cases of 146 ccRCC patients from two medical centers from December 2018 to December 2023, with 117 patients from Hospital and 29 from the *Hospital Affiliated to University of **. Radiomic features were extracted from whole-abdomen CT images, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used for feature selection. The Multi-Layer Perceptron (MLP) approach was employed to construct five predictive models (RAD_NE, RAD_AP, RAD_VP, RAD_Delta1, RAD_Delta2). The models were evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity, while clinical utility was assessed through Decision Curve Analysis (DCA).

RESULTS

A total of 1,834 radiomic features were extracted from the three phases of the CT images for each model. The models demonstrated strong classification performance, with AUC values ranging from 0.837 to 0.911 in the training set and 0.608 to 0.869 in the test set. The Rad_Delta1 and Rad_Delta2 models demonstrated advantages in predicting ccRCC pathological grading.The AUC value of the Rad_Delta1 is 0.911in the training set and 0.771 in the external verifcation set.The AUC value of the Rad_Delta2 is 0.881 in the training set and0.608 in the external verifcation set. DCA curves confirmed the clinical applicability of these models.

CONCLUSION

CT-based delta-radiomics shows potential in predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).

Precision Population Cancer Medicine in Brain Tumors: A Potential Roadmap to Improve Outcomes and Strategize the Steps to Bring Interdisciplinary Interventions

Brain tumors, a significant health burden, rank as the second leading cause of cancer among adolescents and young adults and the eighth most common cancer in older adults. Despite treatment advances, outcomes for many brain tumor types, especially glioblastoma multiforme (GBM), remain poor. Precision population cancer medicine (PPCM) offers promising avenues for improving outcomes in brain tumor management. This comprehensive review delves into the current landscape of brain tumor diagnosis and treatment, with a primary focus on the potential of PPCM to enhance care. The review explores several key areas where PPCM approaches show promise. In genetics and molecular biology, the genetic heterogeneity of brain tumors poses challenges and opportunities for targeted therapies. Understanding genetic patterns can guide treatment strategies and improve prognostication. Epigenetic modifications are crucial in brain tumor development and progression. Deoxyribonucleic acid (DNA) methylation patterns, particularly of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter, serve as essential biomarkers for treatment response and prognosis in GBM. Targeting epigenetic mechanisms could lead to novel therapeutic approaches. Non-invasive liquid biopsy techniques show potential for diagnosis, monitoring, and prognostication in brain tumors. Analysis of circulating tumor DNA and microRNAs may provide valuable information about tumor characteristics and treatment response. Advanced imaging techniques, including radiomics and radiogenomics, combined with artificial intelligence (AI) algorithms, are enhancing tumor detection, characterization, and treatment planning. These technologies can contribute to more personalized treatment approaches. In addition, emerging nanotherapeutic platforms, which involve the use of nanoparticles to deliver drugs directly to tumors, and theranostic approaches, which combine therapy and diagnostics in a single platform, offer new possibilities for targeted drug delivery and real-time treatment monitoring in brain tumors. The review also addresses socioeconomic and demographic factors influencing brain tumor incidence and outcomes. It highlights the stark disparities in care access and survival rates among different racial and ethnic groups, emphasizing the urgent need for PPCM strategies to address these inequities. Challenges in implementing PPCM for brain tumors include the blood-brain barrier, which limits drug delivery, and the need for more extensive clinical trials to validate new approaches. The authors stress the importance of interdisciplinary collaboration and data sharing to advance the field, making the audience feel united and part of a larger team. While PPCM holds great promise, the review emphasizes that it should complement, not replace, population-level interventions and standard-of-care treatments. The authors advocate for a balanced approach that leverages cutting-edge personalized strategies while ensuring broad access to effective treatments. In conclusion, PPCM represents a powerful tool in the fight against brain tumors, offering the potential for more targeted, effective, and less toxic treatments. However, realizing its full potential will require ongoing research, clinical validation, and policy interactions to address disparities in care access.

Precision Population Cancer Medicine in Cancer of the Uterine Cervix: A Potential Roadmap to Eradicate Cervical Cancer

With the success of the Human Genome Project, the era of genomic medicine (GM) was born. Later on, as GM made progress, there was a feeling of exhilaration that GM could help resolve many disease processes. It also led to the conviction that personalized medicine was possible, and a relatively synonymous word, precision medicine (PM), was coined. However, the influence of environmental factors and social determinants of diseases was only partially given their due importance in the definition of PM, although more recently, this has been recognized. With the rapid advances in GM, big data, data mining, wearable devices for health monitoring, telemedicine, etc., PM can be more easily extended to population-level health care in disease management, prevention, early screening, and so on.and the term precision population medicine (PPM) more aptly describes it. PPM's potential in cancer care was posited earlier,and the current authors planned a series of cancer disease-specific follow-up articles. These papers are mainly aimed at helping emerging students in health sciences (medicine, pharmacy, nursing, dentistry, public health, population health), healthcare management (health-focused business administration, nonprofit administration, public institutional administration, etc.), and policy-making (e.g., political science), although not exclusively. This first disease-specific report focuses on the cancer of the uterine cervix (CC). It describes how recent breakthroughs can be leveraged as force multipliers to improve outcomes in CC - by improving early detection, better screening for CC, potential GM-based interventions during the stage of persistent Human papillomavirus (HPV) infection and treatment interventions - especially among the disadvantaged and resource-scarce populations. This work is a tiny step in our attempts to improve outcomes in CC and ultimately eradicate CC from the face of the earth.

Free automatic software for quality assurance of computed tomography calibration, edges and radiomics metrics reproducibility

PURPOSE

To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features.

METHODS

The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient).

RESULTS

Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%).

CONCLUSIONS

An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested.

Lirette S, Yang CC, Mundra E, Woods WC, Otts J, Doherty M, Panter P, Howard C, Ridgway M, Allbright R. The Influence of Patient and System Factors on the Radiotherapy Treatment Time in the Treatment of Non-metastatic Cervical Cancer Patients in a Rural and Resource-Lean State’s Safety-Net Hospital: Benefits of Strategic Planning

Objective To decrease radiotherapy treatment time (RTT), measured from the day of initiation of radiotherapy to the day of its completion, specific strategies were initiated in early 2020 in the only academic safety-net medical center in a rural, resource-lean state. The factors that can succeed and those that need further improvements were analyzed in this initial assessment phase of our efforts to shorten the RTT. Methods This is an analysis of 28 cervix cancer patients treated with magnetic resonance imaging (MRI)-guided brachytherapy (February 2020-November 2021). The relationship between independent and dependent variable were analyzed by simple linear regression, and p-values ≤ 0.05 were considered statistically significant. SPSS software version 28.0 (IBM, Armonk, NY, USA) was used for statistical analysis. Results Two RTT groups (≤ 60 (32.1%) vs. > 60 days {67.9%}) with median RTT of 68 days (range, 51 to 106 days) were analyzed. Caucasians represented 66.7% of the RTT ≤ 60 days group. Four 'issues' were identified that increased the RTT: non-compliance, learning curve (early days of implementation of MRI-guided brachytherapy in the department), stage IV comorbidities, and with more than one issue mentioned; 77.8% with no issues had ≤ 60 days RTT vs. 26.3% for the > 60 days group. The breakdown of the no-issues factor by calendar year showed the RTT of ≤ 60 days was achieved higher in 2021 (85.7% vs. 20.0%; p=0.023) compared to 2020. For this entire cohort, the RTT of ≤ 60 days was achieved higher in 2021 (50.0% vs. 8.3%; p=0.019) compared to 2020. Data also showed improvement in RTT of ≤ 60 days for every sequential six months. 'Non-compliance' and 'learning curve' were the most important factors among patients having the longest RTTs. Conclusion The RTT can be further decreased. As a result of this preliminary analysis of the our strategic planning approach of 'circular' "See it," "Own it," "Solve it," and "Do it" and go back to the first step again, we plan to implement the following strategies in the immediate future to shorten the RTTs further and, in turn, improve our overall outcomes (local/regional control, disease-free survival, and overall survival): (a) Interdigitate MRI-guided brachytherapy during external beam radiotherapy (EBRT); patients who can not get the interdigitated brachytherapy procedures performed during the course of EBRT for any reason will receive two brachytherapy procedures per week; (c) attempt to add a cervix cancer care navigator to our staff to help patients having social issues, thus leading to compliance problems; (d) finally, in a year or two after these new strategic implementations, the RTT data will be reanalyzed.