Prediction Of Therapeutic Outcome Research Articles

Inhibition and excitation in the sexual brain: An useful tool for the prediction of therapeutic outcomes in women with sexual dysfunction

ABSTRACT Introduction The “Dual control model” of sexual response postulates that sexual desire and associated behaviors depend on the balance between excitation and inhibition. These aspects have not been evaluated in women with sexual dysfunction (FSD). Objective To explore the psychosexual correlates of excitation and inhibition in women with FSD, according to their menopausal state. We also aimed at performing a pre-post analysis in a sub-sample of patients followed for 3-6 months, investigating the possible relationships between excitation and inhibition and the changes in questionnaires related to sexual function. Methods In an observational, retrospective study, we recruited a consecutive series of 66 women consulting for FSD. Patients underwent a clinical evaluation, a structured interview and completed several self-administered questionnaires, including Sexual Inhibition/Sexual Excitation Scale (SIS/SES), Female Sexual Function Index (FSFI), Female Sexual Distress Scale-Revised (FSDS-R), Body Uneasiness Test (BUT) and Middlesex Hospital Questionnaire (MHQ). A subgroup of patients (n=28) was evaluated at 3-6 months follow-up, using the FSFI and FSDS-R questionnaires. Results In pre-menopausal women, the SIS1 scale (Sexual inhibition due to the fear of performance failure) was associated with the diagnosis of Hypoactive Sexual Desire Disorder (HSDD), even after adjusting for confounders (p=0.047), whereas in post-menopausal women, we found an association between HSDD and the SES scale (total sexual excitation) (p=0.039; see also Figure 1). Given these correlations, we developed two cut-off values for the SES and SIS1 scales, according to which a score < 49 on the SES (in postmenopausal women) and a score > 34 on the SIS1 (in premenopausal women) accurately identified patients diagnosed with HSDD. In post-menopausal women, the ROC curve for the SES scale showed an accuracy (AUC) of 0.734 +- 0.107, p=0.037, in identifying HSDD; considering a cut-off value of 49, the sensitivity and specificity for HSDD detection were 85.7% and 66.7%, respectively (Fig. 2). In premenopausal women, the ROC curve for the SIS1 scale showed an AUC of 0.853 +- 0.081, p=0.007, in identifying HSDD; considering a cut-off value of 34, the sensitivity and specificity for HSDD detection were 78.6% and 75.0%, respectively (Fig. 3). At follow-up, the mean change in FSFI total score was not correlated with the SES or SIS2 scales (p=0.214 and p=0.402, respectively), but only with the SIS1 scale (p=0.026). When comparing patients with low excitation (SES scale <49) and high inhibition (SIS1 scale >34) with the rest of the sample, they showed a significantly lower change in FSFI total score (p=0.018). Conclusions In women consulting for FSD, sexual excitation and inhibition, which may be easily assessed by the SIS/SES questionnaire, emerge as key aspects in both the diagnostic evaluation and planning of therapeutic outcomes, in the perspective of a personalized sexual medicine approach. Disclosure Work supported by industry: no.

Music therapeutic emotional processing (MEP): Expression, awareness, and pain predict therapeutic outcome

Successful emotional processing is pivotal for the therapeutic change, and music can support emotional processing. However, we know little on how music-based emotional processing actually predicts clinical outcomes. This study investigated music therapeutic emotional processing (MEP) as a predictor of therapeutic outcome in treatment for depression. Data consisted of self-reports of 64 clients (age range 19–57, 74% female) from a clinical trial (12 sessions) of integrative improvisational music therapy (IIMT). A 19-item MEP questionnaire was developed for assessing clients’ experiences after sessions. Emergent MEP factors were correlated with clients’ perceptions of the therapeutic value of the sessions (Session Evaluation Questionnaire [SEQ]) and with recovery from depression (Montgomery–Åsberg Depression Rating Scale [MADRS]). MEP emerged as a constitution of three factors: expression, awareness, and pain. Expression and awareness increased across the process and correlated positively with session value and MADRS change. Pain demonstrated a more complex inverted u-shaped temporal curve and correlated with roughness of session experience. The presence of pain during the late part of the therapy predicted lower recovery. The findings support MEP factors as predictors of therapeutic outcome and provide conceptual insight into the mechanisms of change in art therapies. Knowledge on music-based emotional processing holds relevance also beyond therapy context for everyday music engagement.

Musical interaction in music therapy for depression treatment

Music therapy is efficacious for the treatment of depression. Compared to other psychotherapeutic forms, it allows for the emergence of various modes of mutual interaction, thus enabling multiple channels for emotional expression and fostering therapeutic alliance. Although musical interaction patterns between client and therapist have been regarded as predictors of therapeutic outcome in depression, this has not yet been systematically investigated. We aim to address this gap by analyzing the possible linkage between musical interaction features and changes in depression score. In a clinical trial, digital piano improvisations from 58 Finnish clients and their therapists were recorded over 12 sessions of music therapy lasting 6 weeks. Subsequently, a variety of symbolic features describing pitch, rhythm, duration, and velocity were extracted from the improvisations. We observed a number of relationships between client–therapist interaction and clinical improvement. Clients with largest improvements displayed higher overall interaction, particularly more musical interaction in the middle of the therapy process than in the beginning and end. In contrast, clients with lower depression change score exhibited overall lower interaction and yielded other temporal profiles of interaction. The association between clinical improvement and an inverted U-shaped curve of musical interaction is discussed in the light of process-outcome literature.

Electroclinical features and long-term therapeutic response in patients with typical absence seizures.

To characterize in detail the electroclinical features of typical absence seizures and elucidate whether EEG or semiology features, alone or in combination, can predict long-term therapeutic outcome. We analysed video-EEG recordings from 213 typical absence seizures from 61 patients with idiopathic generalized epilepsy. We extracted semiological features, in addition to hallmark manifestations (motor/behavioural arrest, non-responsiveness), their location, timing and frequency. We evaluated the duration and frequency of generalized spike-wave discharges and the presence of polyspikes. We used a supervised machine-learning approach (random forest) to search for classifier features for long-term therapeutic outcome (>one year). Besides the hallmark manifestations, additional semiological features were identified in 87% of patients (75% of seizures). The most common additional semiological features were automatisms and eye blinking (observed in 45% and 41.5% of seizures, respectively). Automatisms were associated with longer seizure duration, and oral automatisms occurred earlier compared to limb automatisms (4.03 vs. 6.19 seconds; p=0.005). The mean duration of the ictal spike-wave discharges was nine seconds, and the median frequency was 3Hz. Polyspikes occurred in 46 seizures (21.6%), in 19 patients (31%). Median follow-up was five years, and 73% of the patients were seizure-free at the end of the follow-up. None of the semiological features, alone or in combination, were predictors of therapeutic outcome. The only significant classifier was the presence of polyspikes, predicting a non-seizure-free outcome with an accuracy of 73% (95%CI: 70-77%), positive predictive value of 92% (95%CI: 84-98%) and negative predictive value of 60% (95%CI: 39-81%). Semiological features, in addition to behavioural arrest and non-responsiveness, are common in typical absence seizures, but they do not predict long-term therapeutic outcome. The presence of polyspikes has a high positive predictive value for unfavourable therapeutic outcome, and their presence should therefore be included when reporting EEGs in patients with typical absence seizures.

Clinical profile and predictors of therapeutic outcome with azithromycin in pediatric scrub typhus: A study from Eastern India.

In this prospective study, children from 1-month to 12-years, admitted with positive IgM ELISA testing for scrub typhus, were enrolled over 1-year and administered an empirical single dose of azithromycin (10 mg/kg). All 189 (median age 4.84-years) children had fever with median duration of 8 days at admission. Shortness of breath, altered sensorium, headache, lymphadenopathy, hepatosplenomegaly, pallor, oedema and hypotension were the most prominent symptoms/signs. About 46% of children developed complications, among them hepatic, neurological and cardiovascular were most common. Our study showed a remarkable response to azithromycin, with mean defervescence of 32 h and no mortality. The presence of shortness of breath, headache, altered sensorium, hepatosplenomegaly, meningeal signs, severe anemia, leucocytosis, hyponatremia along with/without any of above mentioned three systemic complications were independent predictors for delayed treatment response by multivariate logistic regression analysis. Hepatic, neurological and cardiovascular complications are an emerging trend. Empiric azithromycin based on clinical suspicion of scrub typhus can be lifesaving.

Biomimetic models of the glomerulus.

The use of biomimetic models of the glomerulus has the potential to improve our understanding of the pathogenesis of kidney diseases and to enable progress in therapeutics. Current in vitro models comprise organ-on-a-chip, scaffold-based and organoid approaches. Glomerulus-on-a-chip designs mimic components of glomerular microfluidic flow but lack the inherent complexity of the glomerular filtration barrier. Scaffold-based 3D culture systems and organoids provide greater microenvironmental complexity but do not replicate fluid flows and dynamic responses to fluidic stimuli. As the available models do not accurately model the structure or filtration function of the glomerulus, their applications are limited. An optimal approach to glomerular modelling is yet to be developed, but the field will probably benefit from advances in biofabrication techniques. In particular, 3D bioprinting technologies could enable the fabrication of constructs that recapitulate the complex structure of the glomerulus and the glomerular filtration barrier. The next generation of in vitro glomerular models must be suitable for high(er)-content or/and high(er)-throughput screening to enable continuous and systematic monitoring. Moreover, coupling of glomerular or kidney models with those of other organs is a promising approach to enable modelling of partial or full-body responses to drugs and prediction of therapeutic outcomes.

Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study.

Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

BackgroundDespite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.MethodsWe developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4–11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.ResultsBiologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4–11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4–11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.ConclusionsWe developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.Graphical abstract

Prediction of therapeutic outcomes of female pattern hair loss patients based on clinical features with application of artificial intelligence

Prediction of therapeutic outcomes of female pattern hair loss patients based on clinical features with application of artificial intelligence

Prediction of Therapeutic Outcome in a Naturalistic Setting Using Pretreatment Psychological Distress Indicators

Background: Outcome predictions allow to improve psychotherapy and to increase economic benefit. The efficient translation into practice requires simple prediction methods. The present study evaluates the prediction of treatment outcome based on initial distress level. Methods: Routine data of a university psychotherapy outpatient clinic were used (N = 3,145, M_age = 35.8, 67% female). Low versus high distress patients (classified by overall psychological distress, symptomatology, and previous treatment) were compared on total reduction in psychopathology, (early) response, remission, and premature treatment discontinuation using t tests and logistic regressions. Response and remission were assessed via relative (percentage improvement) and absolute measures (Reliable Change Index; RCI). Results: Distress level was inversely related to percentage improvement (OR = 0.62) and remission (OR = 0.34). It was positively related to total reduction in psychopathology (d = 0.63), RCI response (OR = 2.37), and treatment discontinuation (OR = 2.15). Early response and treatment discontinuation partially mediated the relationship between distress level and treatment outcome. Conclusions: Treatment success tends to be lower when initial distress is high, but this finding appears contingent on the operationalization of treatment outcome. The presented classification approach is easy to implement in practice and may be useful in order to counter an excessive workload in psychotherapy trainees.

Review article: how the intestinal microbiota may reflect disease activity and influence therapeutic outcome in inflammatory bowel disease.

Intestinal bacteria produce metabolites and by-products necessary for homeostasis. Imbalance in this equilibrium is linked to multiple pathologies including inflammatory bowel disease (IBD). The role of the gut microbiota in determining treatment response is becoming apparent, and may act as biomarker for efficacy. To describe knowledge about the intestinal microbiota on disease severity and treatment outcomes in IBD METHODS: Descriptive review using PubMed to identify literature on the intestinal microbiota in IBD RESULTS: Severe IBD has a less diverse microbiota with fewer commensal microbiota communities and more opportunistic pathogenic bacteria originating from the oral cavity or respiratory tract. IBD treatments can alter gut microbiota composition, but in vitro/in vivo studies are needed to prove causation. A diversification of the microbiota is observed during remission. Patients with a more diverse baseline microbiome and higher microbial diversity show better response to anti-tumour necrosis factor-α, vedolizumab and ustekinumab therapy. Higher abundance of short chain fatty acid-producing bacteria, fewer mucus-colonising bacteria and lower abundance of pro-inflammatory bacteria have also been associated with a favourable outcome. Predictive models, based on a combination of microbiota, clinical data and serological markers, have good accuracy for treatment outcome and disease severity. The intestinal microbiota in IBD carries a set of promising biomarkers of disease activity and prediction of therapeutic outcome. Current insights may also help in designing microbiota modulation strategies to improve outcomes in IBD.

Prediction of response to first-line therapy with ITP by flow cytometric analysis of bone marrow lymphocyte phenotypes.

In the present study, we analyzed phenotypes of cells in the lymphocyte region of bone marrow in 68 patients with primary immune thrombocytopenia (ITP) to determine whether cellular phenotype predicts response to first-line therapy (corticosteroids or corticosteroids plus intravenous immunoglobulin). In 52 newly diagnosed ITP patients, an abnormal CD4:CD8 ratio (CD4/CD8 ratio < 0.4 and 2.3 < CD4/CD8 ratio) was noted in 22 patients in the responder group, whereas all non-responder and control individuals showed normal CD4:CD8 ratio (p < 0.001). The absolute number of CD19+ cells in patients with 0.4 ≤ CD4/CD8 ratio ≤ 2.3 or 2.3 < CD4/CD8 ratio was higher than that in other groups. (p = 0.016). In 16 chronic ITP patients, the absolute number of NK cells in the responder group was lower than those in the non-responder and control groups (p = 0.032). An abnormal CD4:CD8 ratio was noted in all patients in the responder group, whereas all patients in non-responder and control groups showed normal CD4:CD8 ratio (p < 0.001). The present results indicate that CD4:CD8 ratio, B cells, and NK cells contribute to the prediction of therapeutic outcomes of ITP patients.

Differential Reflection of the Relationship between Therapeutic Alliance and Outcome in a Day Treatment Hospital Setting

The therapeutic alliance is considered to be a significant and empirically well-documented determinant of therapeutical success. The aim of the present study was to replicate this effect using a large daily clinical sample and to consider different aspects of the therapeutic relationship in an extreme group of particularly low relationship satisfaction separately. A longitudinal examination of a sample of n=809 patients (M=34,32; SD=10,7; 72,6% female) in a day care hospital setting was carried out. Using multiple regression analysis, the link between therapeutic alliance (Helping Alliance Questionnaire; HAQ-S) in the third week and therapeutic outcome (Brief Symptom Inventory-18; BSI-18) was investigated. Analyses have been conducted for the overall sample as well as for the extreme group representing the lowest decile regarding relationship satisfaction and the remaining 90%. A distinction was also made between the 2 subscales of the HAQ, satisfaction with the relationship and satisfaction with the outcome. The therapeutic alliance after 3 weeks was a significant predictor for therapeutic outcome. Within the extreme group with initially low relationship satisfaction, relationship satisfaction proved to be a significant and strong predictor for therapeutic outcome, but was not useable for the prediction of individual cases due to wide confidence interval (β=0,622; 95% CI [0,051; 1,095]). In contrast, the relationship satisfaction in the remaining 90 percent explained no additional variance of therapeutic outcome beyond the explanation by satisfaction with the outcome (β=0,244; 95% CI [0,176; 0,391]). The results emphasize the importance of therapeutic alliance also in a day care hospital setting. Relationship satisfaction plays a central, other factors exceeding role in the prediction of therapeutic outcome only in the extreme group of particularly dissatisfied patients. Ensuring at least a sufficiently good therapeutic relationship is of great importance and therefore requires early identification and, if necessary, intervention.

Scientific frontiers in faecal microbiota transplantation: joint document of Asia-Pacific Association of Gastroenterology (APAGE) and Asia-Pacific Society for Digestive Endoscopy (APSDE)

ObjectiveThe underlying microbial basis, predictors of therapeutic outcome and active constituent(s) of faecal microbiota transplantation (FMT) mediating benefit remain unknown. An international panel of experts presented key elements that will...

Abstract 2301: Antiglycan antibodies as predictors of therapeutic outcome in cervical cancer patients

Abstract Cervical cancer is a significant cause of cancer related death among women, with the highest mortality rates in developing countries. We aim to identify serum biomarkers for predicting outcome of radio therapy alone or combination of radiotherapy and brachytherapy. We measured natural anti-glycan IgG antibodies in the serum of stage II (n=276) and III (n=292) cervical cancer patients treated with radiation alone or in combination with brachytherapy on a suspension glycan array containing 184 glycans. Overall and progression free survival was used to determine the treatment outcome by Kaplan-Meier and Cox proportional hazards ratio. Significant protective effects were observed for Blood Group H antigens (HR=0.642, p=0.0013), Lewis Antigens (0.72, 0.015; 0.654, 0.003), Galili antigens (0.671, p=0.004), and other glycans belonging to Isoglobo (0.671,0.004) and 3fucosyllactose series (0.687,0.004). The protective effects remained significant after adjusting for stage and treatment. Our data suggest that measurement of natural anti-glycan IgG antibodies in serum can predict outcome of therapy in cervical cancer patients. Citation Format: SHARAD PUROHIT, Paul M. Tran, Lynn K. Tran, Xuezheng Song, Ferris G. Ferris, John J. Wallbillich, Boying Dun, Jin-Xiong She. Antiglycan antibodies as predictors of therapeutic outcome in cervical cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2301.

Dynamic Contrast-enhanced Area-detector CT vs Dynamic Contrast-enhanced Perfusion MRI vs FDG-PET/CT: Comparison of Utility for Quantitative Therapeutic Outcome Prediction for NSCLC Patients Undergoing Chemoradiotherapy.

Purpose:To directly compare the utility for therapeutic outcome prediction of dynamic first-pass contrast-enhanced (CE)-perfusion area-detector computed tomography (ADCT), MR imaging assessed with the same mathematical method and 2-[fluorine-18]-fluoro-2-deoxy-d-glucose–positron emission tomography combined with CT (PET/CT) for non-small cell lung cancer (NSCLC) patients treated with chemoradiotherapy.Materials and Methods:Forty-three consecutive stage IIIB NSCLC patients, consisting of 25 males (mean age ± standard deviation: 66.6 ± 8.7 years) and 18 females (66.4 ± 8.2 years) underwent PET/CT, dynamic CE-perfusion ADCT and MR imaging, chemoradiotherapy, and follow-up examination. In each patient, total, pulmonary arterial, and systemic arterial perfusions were calculated from both perfusion data and SUVmax on PET/CT, assessed for each targeted lesion, and averaged to determine final values. Receiver operating characteristics analyses were performed to compare the utility for distinguishing responders from non-responders using Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 criteria. Overall survival (OS) assessed with each index were compared between two groups by means of the Kaplan–Meier method followed by the log-rank test.Results:Area under the curve (Az) for total perfusion on ADCT was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Az of total perfusion on MR imaging was significantly larger than that of pulmonary arterial perfusion (P < 0.05). Mean OS of responder and non-responder groups were significantly different for total and systemic arterial (P < 0.05) perfusion.Conclusion:Dynamic first-pass CE-perfusion ADCT and MR imaging as well as PET/CT are useful for early prediction of treatment response by NSCLC patients treated with chemoradiotherapy.

Patterns of Membrane Protein Clustering in Peripheral Lymphocytes as Predictors of Therapeutic Outcomes in Major Depressive Disorder.

There is an utmost necessity of developing novel biomarkers of depression that result in a more efficacious use of current antidepressant drugs. The present report reviews and discusses a recent series of experiments that focused on analysis of membrane protein clustering in peripheral lymphocytes as putative biomarkers of therapeutic efficacy for major depressive disorder. This review recapitulates how the ideas were originated, and the main findings demonstrated that analysis of serotonin transporter and serotonin 2 A receptor clustering in peripheral lymphocytes of naïve depression patients resulted in a discrimination of two subpopulations of depressed patients that showed a differential response upon 8 weeks of antidepressant treatment. The paper also reviews the usefulness of animal models of depression for an initial evaluation of membrane protein clustering in lymphocytes, which provides a screening tool to determine additional proteins to be further evaluated in depression patients. Finally, the present review provides a brief discussion of the general field of biomarkers of depression in relation to therapeutic outcomes and suggests additional ideas to provide extra value to the reviewed studies.

223Ra-dichloride therapy of bone metastasis: optimization of SPECT images for quantification

Background223Ra imaging is crucial to evaluate the successfulness of the therapy of bone metastasis of castration-resistant prostate cancer (CRPC). The goals of this study were to establish a quantitative tomographic 223Ra imaging protocol with clinically achievable conditions, as well as to investigate its usefulness and limitations.We performed several experiments using the Infinia Hawkeye 4 gamma camera (GE) and physical phantoms in order to assess the optimal image acquisition and reconstruction parameters, such as the windows setting, as well as the iteration number and filter of the reconstruction algorithm. Then, based on the MIRD pamphlet 23, we used a NEMA phantom and an anthropomorphic TORSO® phantom to calibrate the gamma camera and investigate the accuracy of quantification.ResultsExperiences showed that the 85 keV ± 20%, 154 keV ± 10%, and 270 keV ± 10% energy windows are the most suitable for 223Ra imaging.The study with the NEMA phantom showed that the OSEM algorithm with 2 iterations, 10 subsets, and the Butterworth filter offered the best compromise between contrast and noise. Moreover, the calibration factors for different sphere sizes (26.5 ml, 11.5 ml, and 5.6 ml) were constant for 223Ra concentrations ranging between 6.5 and 22.8 kBq/ml. The values found are 73.7 cts/s/MBq, 43.8 cts/s/MBq, and 43.4 cts/s/MBq for 26.5 ml, 11.5 ml, and 5.6 ml sphere, respectively. For concentration lower than 6.5 kBq/ml, the calibration factors exhibited greater variability pointing out the limitations of SPECT/CT imaging for quantification.By the use of a TORSO® phantom, we simulated several tumors to normal tissue ratios as close as possible to clinical conditions. Using the calibration factors obtained with the NEMA phantom, for 223Ra concentrations higher than 8 kBq/ml, we were able to quantify the activity with an error inferior to 18.8% in a 5.6 ml lesion.ConclusionsAbsolute quantitative 223Ra SPECT imaging appears feasible once the dimension of the target is determined. Further evaluation should be needed to apply the calibration factor-based quantitation to clinical 223Ra SPECT/CT imaging. This will open the possibility for patient-specific 223Ra treatment planning and therapeutic outcome prediction in patients.

Enhancement of Sensitivity to Chemo/Radiation Therapy by Using miR-15b against DCLK1 in Colorectal Cancer.

SummaryChemo-/radiotherapy resistance is the main cause accounting for most treatment failure in colorectal cancer (CRC). Tumor-initiating cells (TICs) are the culprit leading to CRC chemo-/radiotherapy resistance. The underlying regulation mechanism of TICs in CRC remains unclear. Here we discovered that miR-15b expression positively correlated with therapeutic outcome in CRC. Expression of miR-15b in pretreatment biopsy tissue samples predicted tumor regression grade (TRG) in rectal cancer patients after receiving neoadjuvant radiotherapy (nRT). Expression of miR-15b in post-nRT tissue samples was associated with therapeutic outcome. DCLK1 was identified as the direct target gene for miR-15b and its suppression was associated with self-renewal and tumorigenic properties of DCLK1+ TICs. We identified B lymphoma Mo-MLV insertion region l homolog (BMI1) as a downstream target regulated by miR-15b/DCLK1 signaling. Thus, miR-15b may serve as a valuable marker for prognosis and therapeutic outcome prediction. DCLK1 could be a potential therapeutic target to overcome chemo-/radioresistance in CRC.

Radiogenomics.

Radiogenomics is the study of genomic changes that underlie the radioresponse of normal and tumor tissues. And while this is generally regarded as a whole genome approach, one must keep in mind the impact of single gene biology on radioresponse, (ataxia telangiectasia, Nijmegen breakage syndrome). This review begins with the association of single nucleotide polymorphisms in the DNA with adverse normal tissue events to the prediction of therapeutic outcome after radiotherapy. From there it covers transcriptome (protein coding RNA transcripts) analysis, which is where the greatest understanding of the molecular signaling responsible for the radioresponse of tumors and normal tissues is known. Non-protein coding RNA transcripts (miRNA, lncRNA), transcribed from what was once thought of as junk DNA, are now known to be negative regulators of the transcription of mRNA by multiple mechanisms. miRNA can act as tumor suppressors or oncogenes regulating a diverse range of cellular processes that drive radioresponse and biosignatures that predict outcome after radiotherapy are described. Biological signatures that explain differences in radioresponse based upon cell type, biological signatures that predict surviving fraction at 2Gy and signatures that identify hypoxia have been described. The omics analysis of the response of mammalian cells to charged particle, predominantly proton and carbon ions, is less mature than that seen with low LET radiation exposures. However, there appear to be responses after charged particle exposure that parallel the responses seem with low LET exposures. This commonality of response is centered around the downstream signaling of p53. There are also novel omics responses to charged particles that help explain the response of tumors to charged particle exposures. For instance, signaling pathways associated with angiogenesis, vasculogenesis, migration and invasion appear to be downregulated in a number of cell types when exposed to charged particles. This response supports both invitro and invivo data suggesting that tumors exposed to charged particles are less invasive, unlike the response of tumors to low LET exposures. Profoundly lacking for low LET and charged particle exposures are predictive or prognostic signatures of radioresponse or tumor physiology affecting radioresponse that have been validated in prospective clinical trials. For example, the identification of low LET tumor radioresistance could be used as a marker of patient eligibility for carbon therapy. Tissue specific signatures, or accurate imaging of hypoxic regions, could be used for charged particle selection to overcome hypoxia per se, or could be used to prescribe a high LET therapeutic boost to a hypoxic region of a tumor. Integrating radiogenomics into radiation oncology has the potential to personalize an already precise form of cancer therapy.