The role of imaging has been increasing in pretherapy planning and response assessment in cervical cancer, particularly in high-resource settings that provide access to computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). In 2018, imaging was incorporated into the International Federation of Gynecology and Obstetrics staging system for cervical cancer. Magnetic resonance imaging is advantageous over CT for evaluation of the primary cervical cancer size and extent, because of superior contrast resolution. Furthermore, quantitative methods, including diffusion-weighted and dynamic contrast-enhanced MRI, show promise in improving treatment response and prognosis evaluation. Molecular imaging with fluorodeoxyglucose–PET/CT and PET/MRI can be particularly helpful in the detection of nodal disease and distant metastases. Semiautomated delineation of 3-dimensional tumor regions of interest has facilitated the development of novel PET-derived biomarkers that include metabolic volume and radiomics textural analysis features for prediction of outcomes. However, posttreatment inflammatory changes can be a confounder and lymph node evaluation is challenging, even with the use of PET/CT. Liquid biopsy has emerged as a promising tool that may be able to overcome some of the drawbacks inherent with imaging, such as limited ability to detect microscopic metastases or to distinguish between postchemoradiotherapy changes and residual tumor. Preliminary evidence suggests that liquid biopsy may be able to identify cervical cancer treatment response and resistance earlier than traditional methods. Future work should prioritize how to best synergize imaging and liquid biopsy as an integrated approach for optimal cervical cancer management.

Orbital metastases are a rare but life-altering complication in cancer. Most commonly seen in breast cancer, metastases to the optic nerves or extraocular muscles can have a devastating impact on visual acuity and quality of life. Hormone receptor status plays a central role in metastatic breast cancer treatment, with endocrine therapy often representing first-line therapy in hormone-receptor-positive cancers. Staging and treatment response evaluation with positron emission tomography (PET) computed tomography (CT) imaging with 18F-fluorodeoxyglucose (18F-FDG) is limited by high physiologic uptake in the intracranial and intraorbital compartments. Thus, traditional staging scans with 18F-FDG PET/CT may under-detect intraorbital and intracranial metastatic disease and inaccurately evaluate active metastatic disease burden. In comparison, 18F-fluoroestradiol (18F-FES) is a novel estrogen-receptor-specific PET radiotracer, which more accurately assesses the intracranial and intraorbital compartments in patients with estrogen-receptor-positive (ER+) cancers than 18F-FDG, due to lack of physiologic background activity in these regions. We present two cases of breast cancer patients with orbital metastases confirmed on MR imaging who underwent PET/CT imaging with 18F-FES and 18F-FDG. Multimodality imaging with 18F-FES PET/CT offers higher detection sensitivity of orbital metastases, compared with traditional 18F-FDG PET/CT imaging, and can improve the assessment of treatment response in patients with estrogen-receptor-positive (ER+) cancers.

[¹²³I]FP-CIT SPECT has been valuable for distinguishing Parkinson disease (PD) from essential tremor. However, its performance for quantitative assessment of motor dysfunction has not been established. A virtual reality (VR) application was developed and compared with [¹²³I]FP-CIT SPECT/CT for detection of severity of motor dysfunction. Forty-four patients (21 males, 23 females, age 64.5 ± 12.4) with abnormal [¹²³I]FP-CIT SPECT/CT underwent assessment of bradykinesia, activities of daily living, and tremor with VR. Support vector machines (SVM) machine learning models were applied to VR and SPECT data. Receiver operating characteristic (ROC) analysis demonstrated greater area under the curve (AUC) for VR (0.8418, 95% CI 0.6071–0.9617) compared with brain SPECT (0.5357, 95% CI 0.3373–0.7357, p = 0.029) for detection of motor dysfunction. Logistic regression identified VR as an independent predictor of motor dysfunction (Odds Ratio 326.4, SE 2.17, p = 0.008). SVM for prediction of the Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III) demonstrated greater R-squared of 0.713 (p = 0.008) for VR, compared with 0.0764 (p = 0.361) for brain SPECT. This study demonstrates that VR can be safely used in patients prior to [¹²³I]FP-CIT SPECT imaging and may improve prediction of motor dysfunction. This test has the potential to provide a simple, objective, quantitative analysis of motor symptoms in PD patients.

Objective: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients. The purpose of this study was to assess the value of gated myocardial perfusion single-photon emission computed tomography (GMPS) phase analysis for predicting survival in heart failure patients undergoing CRT. Methods: This retrospective cohort study evaluated heart failure patients who underwent GMPS prior to CRT. Phase histogram bandwidth (PHB) and phase SD (PSD) were calculated using GMPS data. Cox proportional hazards model was used to identify independent predictors of overall survival (OS). Results: A total of 35 patients (age 65.1 ± 13.3, 27 men and 8 women), who were followed for mean of 4.1 ± 2.9 years, were enrolled in the study. PSD of greater than 45° was found to be an independent predictor of poor OS (hazard ratio = 12.63, P = 0.011) when compared with age (hazard ratio = 1.00, P = 0.922), gender (hazard ratio = 0.31, P = 0.155), NYHA class (hazard ratio = 0.45, P = 0.087), QRS duration greater than 150 ms (hazard ratio = 2.38, P = 0.401), pre-CRT left ventricular ejection fraction (LVEF) (hazard ratio = 0.95, P = 0.175) and etiology of heart failure (hazard ratio = 1.42, P = 0.641). Furthermore, PHB greater than 140° was also found to be an independent predictor of poor OS (hazard ratio = 5.63, P = 0.040) when compared with age, gender, NYHA class, QRS duration greater than 150 ms, pre-CRT LVEF and etiology of heart failure. Conclusions: PSD and PHB, measured by GMPS, may serve as biomarkers for the prediction of survival in patients undergoing CRT.

Objective: Parkinson disease (PD) is a progressive neurodegenerative disorder with highly variable progression rates. Accurate prediction of rapid disease progression is critical for patient management and clinical trial design. This study evaluates the use of machine learning (ML) models to predict rapid PD progression using integrated clinical and imaging biomarkers. Methods: A retrospective analysis was conducted on 683 patients from the Parkinson’s Progression Markers Initiative (PPMI). Mixed effects models for longitudinal data were used to determine PD progression rates, with rapid progression of PD (RPPD) defined as exceeding the cohort mean rate of change in either the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) total score or the lowest Putamen-Specific Binding ratio (SBR) on I-123 Ioflupane single photon emission tomography (SPECT). The minimum redundancy maximum relevance (MRMR) algorithm identified the most predictive features from a total of 123 features. Support vector machines (SVM) and decision tree (DT) models were trained and evaluated using 5-fold cross-validation and receiver operating characteristic area under the curve (ROC AUC) analysis. Results: A total of 193 patients (135 males and 68 females) were included. The most predictive feature for RPPD was the lowest putamen SBR (MRMR score=0.44, P <0.001). The SVM model achieved an ROC AUC of 0.86 (95% CI: 0.78-0.91) using the 6 highest-ranked MRMR features, whereas the DT model, using only 2 clinically available features (UPDRS total score and lowest putamen SBR), achieved an ROC AUC of 0.81 (95% CI: 0.73-0.86). Conclusions: Machine learning models, particularly SVM, demonstrate strong predictive performance in identifying rapid PD progression using multimodal clinical and imaging biomarkers. The integration of SPECT imaging with clinical assessments improves disease trajectory characterization, potentially supporting personalized treatment strategies and optimizing clinical trial design. Future studies should incorporate additional biomarkers and longer follow-up durations to enhance model generalizability.