Metabolic tumor parameters complement clinicopathological factors in prognosticating advanced stage Hodgkin Lymphoma

Objective(s): Advanced Hodgkin Lymphoma has a higher probability of relapse and recurrence. Classical clinicopathological parameters including the International Prognostic Score (IPS) have not been reliable in predicting prognosis or tailoring treatment.  Since FDG PET/CT is the standard of care in staging Hodgkin Lymphoma, this study attempted to evaluate the clinical utility of baseline metabolic tumor parameters in a cohort of advanced Hodgkin lymphoma (stage III and IV).Methods: Histology-proven advanced Hodgkin Patients presenting to our institute between 2012-2016 and treated with chemo-radiotherapy (ABVD / AEVD) were followed up till 2019. Quantitative PET/CT and clinicopathological parameters were used to estimate the Event Free Survival (EFS) in 100 patients. Kaplan-Meier method with log-rank test was used to compare the survival times of prognostic factors.Results: At a median follow-up of 48.83 months (IQR:33.31-63.05 months), the five-year-EFS was 81%. Of the 100 patients, 16 had relapsed (16%) and none died at the last follow-up. On Univariate analysis, among non-PET parameters bulky disease (P=0.03) and B-symptoms (P=0.04) were significant while among PET/CT parameters SUVmax (p=0.001), SUVmean (P=0.002), WBMTV2.5 (P<0.001), WBMTV41% (P<0.001), WBTLG2.5 (P<0.001) and WBTLG41% (P <0.001) predicted poorer EFS.  5-year EFS for patients with low WBMTV2.5 [<1038.3 cm3] was 89% and 35% for patients with high WBMTV2.5 [≥1038.3 cm3] (p <0.001). In a multivariate model, only WBMTV2.5 (P=0.03) independently predicted poorer EFS.Conclusion: PET-based metabolic parameter (WBMTV2.5) was able to prognosticate and complement the classical clinical prognostic factors in advanced Hodgkin Lymphoma. This parameter could have a surrogate value for prognosticating advanced Hodgkin lymphoma. Better prognostication at baseline translates to tailored or risk-modified treatment and hence higher survival

Complex ferromagnetic state and magnetocaloric effect in single crystalline Nd_{0.7}Sr_{0.3}MnO_{3}

The magnetocaloric effect in single crystalline Nd_{0.7}Sr_{0.3}MnO_{3} is investigated by measuring the field-induced adiabatic change in temperature which reveals a single negative peak around 130 K well below the Curie temperature (T_C=203 K). In order to understand this unusual magnetocaloric effect, we invoke the reported {55}^Mn spin-echo nuclear magnetic resonance, electron magnetic resonance and polarized Raman scattering measurements on Nd_{0.7}Sr_{0.3}MnO_{3}. We show that this effect is a manifestation of a competition between the double exchange mechanism and correlations arising from coupled spin and lattice degrees of freedom which results in a complex ferromagnetic state. The critical behavior of Nd_{0.7}Sr_{0.3}MnO_{3} near Curie temperature is investigated to study the influence of the coupled degrees of freedom. We find a complicated behavior at low fields in which the order of the transition could not be fixed and a second-order-like behavior at high fields.Comment: Accepted for publication in Phys. Rev.

Toward Improved Outcomes for Patients With Lung Cancer Globally: The Essential Role of Radiology and Nuclear Medicine

PURPOSE Key to achieving better population-based outcomes for patients with lung cancer is the improvement of medical imaging and nuclear medicine infrastructure globally. This paper aims to outline why and spark relevant health systems strengthening. METHODS The paper synthesizes the global lung cancer landscape, imaging referral guidelines (including resource-stratified ones), the reliance of TNM staging upon imaging, relevant multinational health technology assessments, and precisely how treatment selection and in turn patient outcomes hinge upon imaging findings. The final discussion presents data on current global gaps in both diagnostics (including imaging) and therapies and how, informed by such data, improved population-based outcomes are tangible through strategic planning. RESULTS Imaging findings are central to appropriate lung cancer patient management and can variably lead to life-prolonging interventions and/or to life-enhancing palliative measures. Early-stage lung cancer can be treated with curative intent but, unfortunately, most patients with lung cancer still present at advanced stages and many patients lack access to both diagnostics and therapies. Furthermore, half of lung cancer cases occur in low- and middle-income countries. The role of medical imaging and nuclear medicine in lung cancer management, as outlined herein, may help inform strategic planning. CONCLUSION Lung cancer is the number one cancer killer worldwide. The essential role that medical imaging and nuclear medicine play in early diagnosis and disease staging cannot be overstated, pivotal in selecting the many patients for whom measurably improved outcomes are attainable. Prevention synergized with patient-centered, compassionate, high-quality lung cancer management provision mandate that strategic population-based planning, including universal health coverage strategies, should extend well beyond the scope of disease prevention to include both curative and noncurative treatment options for the millions afflicted with lung cancer

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Combined PET and Biopsy Evidence of Marrow Involvement Improves Prognostic Prediction in Diffuse Large B-Cell Lymphoma

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Needle(s) in the Haystack – Synchronous Multifocal Tumor Induced Osteomalacia

This is the author accepted manuscript. The final version is available from Endocrine Society via http://dx.doi.org/10.1210/jc.2015-3854MG is supported by the NIHR Cambridge Biomedical Research Centre

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features

Diagnostic performance and clinical impact of Ga-68-PSMA-11 PET/CT imaging in early relapsed prostate cancer after radical therapy: a prospective multicenter study (IAEA-PSMA study)

Biochemical recurrence (BCR) is a clinical challenge in prostate cancer (PCa) patients, as recurrence localization guides subsequent therapies. The use of PET with prostate-specific membrane antigen (PSMA) provides better accuracy than conventional imaging practice. This prospective, multicenter, international study was performed to evaluate the diagnostic performance and clinical impact of PSMA PET/CT for evaluating BCR in PCa patients in a worldwide scenario. METHODS : Patients were recruited from 17 centers in 15 countries. Inclusion criteria were histopathologically proven prostate adenocarcinoma, previous primary treatment, clinically established BCR, and negative conventional imaging (CT plus bone scintigraphy) and MRI results for patients with PSA levels of 4-10 ng/mL. All patients underwent PET/CT scanning with 68Ga-PSMA-11. Images and data were centrally reviewed. Multivariate logistic regression analysis was applied to identify the independent predictors of PSMA-positive results. Variables were selected for this regression model on the basis of significant associations in the univariate analysis and previous clinical knowledge: Gleason score, the PSA level at the time of the PET scan, PSA doubling time, and primary treatment strategy. All patients were monitored for a minimum of 6 mo. RESULTS : From a total of 1,004 patients, 77.7% were treated initially with radical prostatectomy and 22.3% were treated with radiotherapy. Overall, 65.1% had positive PSMA PET/CT results. PSMA PET/CT positivity was correlated with the Gleason score, PSA level at the time of the PET scan, PSA doubling time, and radiotherapy as the primary treatment (P < 0.001). Treatment was modified on the basis of PSMA PET/CT results in 56.8% of patients. PSMA PET/CT positivity rates were consistent and not statistically different among countries with different incomes. CONCLUSION : This multicenter, international, prospective trial of PSMA PET/CT confirmed its capability for detecting local and metastatic recurrence in most PCa patients in the setting of BCR. PSMA PET/CT positivity was correlated with the Gleason score, PSA level at the time of the PET scan, PSA doubling time, and radiotherapy as the primary treatment. PSMA PET/CT results led to changes in therapeutic management in more than half of the cohort. The study demonstrated the reliability and worldwide feasibility of PSMA PET/CT in the workup of PCa patients with BCR.Partially funded by IAEA.https://jnm.snmjournals.orghj2023Nuclear Medicin