Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

Breast Diseases

Skin Diseases

Patients with invasive breast cancer who have clinically negative axilla and receive neoadjuvant treatment followed by sentinel lymph node biopsy are eligible for this study.

Qi-jun Zheng, MD

Ying-jian He, MD

This is a prospective, single-center, non-randomized, non-controlled observational study.

The panel of the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017 strongly believed that sentinel lymph node biopsy to be appropriate and favored the biopsy be carried out after neoadjuvant treatment in patients who presented with a clinically negative axilla and who received neoadjuvant treatment.

Patients with clinically node-negative early breast cancer treated in Breast Cancer Prevention and Treatment Center Peking University Cancer Hospital from February 2013 to August 2017 were retrospectively analyzed to investigate whether sentinel lymph node (SLN) biopsy after neoadjuvant chemotherapy can reduce the positive rate of SLN and the rate of axillary lymph node dissection (ALND). The study group underwent SLN biopsy after neoadjuvant chemotherapy, and the control group underwent SLN biopsy before chemotherapy. A total of 395 patients were eligible, including 85 in the study group and 310 in the control group. The detection rate of SLNs was 95.3% vs 98.7%（χ2 = 3.922，P = 0.069） in the study group compared with the control group, the positive rate of SLNs was 1.2% vs 18.6%（χ2 = 15.207，P = 0.000）, and the rate of ALND was 5.9% vs 15.2%（χ2 = 5.024，P = 0.025） in univariate analysis. Multivariate analysis showed that the study group had lower SLN positive rate (OR = 0.052, 95% CI: 0.0070 to 0.38, P = 0.004) and ALND rate (OR = 0.310, 95% CI: 0.118 to 0.812, P = 0.017). There was no statistically significant difference in detection rate of SLNs （OR = 0.244, 95% CI: 0.058 to 1.021, P = 0.053）between the two groups.

The aim of this study is to explore the detection rate and positive rate of sentinel lymph nodes, the rate of axillary lymph node dissection, and the proportion of patients with axillary lymph node metastases after neoadjuvant chemotherapy in patients with clinically node-negative early breast cancer. A mathematical model of axillary lymph node status will be established to predict axillary lymph node metastases.

Axillary lymph node status are determined in SLNB(sentinel lymph node biopsy) and surgical specimen

Sentinel Lymph Node Biopsy in Clinically Node-negative Early Breast Cancer Patients After Neoadjuvant Chemotherapy

Positive rate of axillary sentinel lymph nodes in patients with clinically node-negative early breast cancer after neoadjuvant chemotherapy will be assessed.

Detection rate of axillary sentinel lymph nodes in patients with clinically node-negative early breast cancer after neoadjuvant chemotherapy will be assessed.

Rate of axillary lymph node dissection in patients with clinically node-negative early breast cancer after neoadjuvant chemotherapy will be assessed.

Proportion of patients with axillary lymph node metastases in patients with clinically node-negative early breast cancer after neoadjuvant chemotherapy will be assessed.

A mathematical model of axillary lymph node status will be established to predict axillary lymph node metastases in patients with early stage axillary cancer after neoadjuvant chemotherapy.

Peking University

Director of Breast Center of Peking University Cancer Hospital

Participants have to fulfill psychological questionnaires in different time points during medical oncological treatment

psychological questionnaires

Ketti Mazzocco

ketti mazzocco

Breast cancer accounts for 28% of all cancer cases in Europe ("WHO," 2018). Coping with breast cancer is becoming an increasingly burdensome socio-economic challenge, partly due to the increasing incidence registered in the last years, which is occurring despite continuous advances in medicine. This said, mortality rate has decreased significantly as 5-year survival rate has risen from 75% to 90% ("Breast Cancer Research Foundation," 2016). For these reasons the number of cancer survivors has grown, with important effects on quality of life even years after the end of treatments.

The process of successful adaptation to breast cancer and the various accompanying stressors can be conceptually defined as the person's resilience. Resilience is a complex construct that can be defined on different levels: an individual's potential (capacity to engage in adaptive coping processes), a process (adaptive reactions to adversity), and an outcome (the final state achieved as the result of coping).

While theoretical contributions regarding resilience models in medical settings have been advanced (Deshields et al., 2016), to date no one has tested the integrated contributing role of multiple psychological, biological and functional variables in predicting the patient's ability to bounce back from the stressful life event of being diagnosed with breast cancer. Resilience is going to be measured through a data-driven method (computation of resilience index on the basis of retrospective data) and through a psychometric method (various domains of resilience through questionnaires).

There is a growing need for novel strategies to improve the understanding and the capacity to predict resilience of women to the variety of stressful experiences.

BOUNCE European Project (H2020 European Project "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE back"; Grant Agreement Nr: 777167) will bring together modelling, medical, and social sciences experts to advance current knowledge on the dynamic nature of resilience as it relates to improved quality of life.

The broad and general objective of the BOUNCE project is to build a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. The investigators will collect biomedical status (BMS), the psychosocial status (PSS) and the functional status (FUS) of breast cancer patients that literature demonstrated to be predictive of patients' capacity to bounce back during the highly stressful treatment and recovery period following diagnosis of breast cancer. The overarching goal of the model is to understand which factors predict optimal adjustment to breast cancer and which combination of factors undermine adjustment. This would allow early identification of women at risk for which it is better to intervene through a personalised psychological support.

Funding: The BOUNCE Project "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE back" has received funding from the European Union's Horizon 2020 research and innovation programme, Project type: H2020 - SCI-2017-CNECT-2; Project type: H2020 - SCI-2017-CNECT-2; Grant Agreement Nr: 777167.

Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back

Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back: a Multicentre Clinical Pilot Study

Assessed by the Connor Davidson Resilience Scale (CD-RISC). The scale consists in ten item on a 5-point Likert scale, ranging from 0 (not true at all) to 4 (true nearly all the time).

Assessed by the Ten Item Personality Inventory (TIPI). The scale consists in 10-item (measure of the Big Five dimensions) on a 7-point Likert scale, ranging from 1(Disagree strongly) to 7 (Agree strongly)

Assessed by the Life Orientation Test-Revised (LOT-R). The scale is a 10-item scale that measures optimistic and pessimistic disposition. Respondents use a 5-point rating scale (0 = strongly disagree; 4 = strongly agree) to show how much they agree with 10 statements about positive and negative expectations.

Assessed by the Sense of Coherence (SOC) 13 item scale. The response alternatives are a semantic scale of 1 point to 7 points, where 1 and 7 indicate extreme feelings about questions (and statements) about how one's life is experienced

Assessed by the PCL-5. The PCL-5 is a 20-item self-report measure that assesses the 20 DSM-5 symptoms of PTSD. Respondents use a 5-point Likert scale (0 = "Not at all" to 4 = "Extremely")

Assessed by the Posttraumatic Growth Inventory (PTGI)10 item, developed to assess growth following traumatic events. The scale consists on a 6-point Likert scale

Assessed by the Perceived ability to cope with trauma (PACT). The questionnaire has two scales that measure the perceived ability to focus on processing the trauma (trauma focus) and to focus on moving beyond the trauma (forward focus). Respondents use a 7-point scale ranging from 1 (not at all able) to 7 (extremely able)

Assessed by the Cognitive Emotion Regulation Questionnaire 18 items(CERQ). The questionnaire is constructed in order to identify the cognitive emotion regulation strategies used after a negative events or situations. Responsens are given on a 5-point Likert scale ranging from 1 (almost never) to 5 (almost always).

Assessed by the Mindful attention awareness scale (MAAS). The scale consists in 15 item (1-6 Likert scale, from 1 "almost always" to 6 "almost never" ) designed to assess open or receptive awareness of and attention to what is taking place in the present.

Assessed by the mini mental adjustment to cancer (mini-MAC). The scale consists in 29 statements allowing the identification and evaluation of four types of strategies of coping with disease. The mini-MAC scale is a self-descriptive tool. The respondent use a four-point scale, to what extent a given statement applies at present.

Assessed by the modified Medical Outcome Study -Social Support survey(mMOS-SS). The mMOS-SS assess the emotional and the instrumental social support.

The Mini-MAC items are rated on a 4-point Likert scale ranging from "Definitely does not apply to me" (1) to "Definitely apply to me" (4).

Assessed by the Family resilience questionnaire (F.A.R.E). The FaRE Questionnaire is designed to measure family resilience in oncological settings. The scale has 24 item on a 7-point Likert scale (from strongly agree to strongly disagree).

Assessed by the Illness perception questionnaire revised (IPQ-r). The illness perceptions questionnaire measures an individual's beliefs and feelings about their illness. The scale consists in 84 items on a 5-point Likert style scale, ranging from strongly disagree to strongly agree.

Assessed by the Cancer behavior inventory-brief (CBI-B), a measure of self-efficacy for coping in cancer patients. The instrument consists of 12 items (rated 1 = not at all confident to 7 = totally confident).

Assessed by the Hospital Anxiety and Depression Scale (HADS). is a 14-item self-report measure designed to assess depression and anxiety. Respondents are asked to rate each statement in considering the previous week on a 0-4 scale that taps into frequency.

Assessed by the European Organization for Research and Treatment of Cancer quality of life questionnaire-Core 30 (EORTC QLQ-C30). The EORTC QLQ-C30 includes 30 items that are rated on a 4-point scale that ranges from 1 "not at all" to 4 "very much". The EORTC QLQ-C30 has a global score, 5 functional scales (physical, role, emotional, cognitive and social), 3 symptoms scales (fatigue, nausea/vomiting, pain), and 6 single items (dyspnea, insomnia, appetite loss, constipation, diarrhea and financial difficulties).

Assessed by the European Organization for Research and Treatment of Cancer (EORTC) Breast Cancer-Specific Quality of Life Questionnaire (QLQ-BR23. The scale consists in 23 item on a likert scale from 1 "not at all" to 4 "very much", assessing body image, sexual functioning, sexual enjoyment, future perspective, systemic therapy side effects, breast symptoms, arm symptoms and upset by hair loss.

Assessed by the Fear of Cancer Recurrence Inventory-Short Form (FCRI-SF). The scale consists in 9 items rated on a Likert scale ranging from 0 ("not at all" or "never") to 4 ("a great deal' or "all the time").

Assessed by the Positive and Negative Affect Schedule-short form (PANAS-FS). The scale consists of two 5-item scales to measure both positive and negative affect. Each item is rated on a 5-point scale of 1 (not at all) to 5 (very much).

Assessed by the Distress Thermometer (DT). The scale consists in a single-item tool using a 0 (no distress) to 10 (extreme distress)-point Likert scale resembling a thermometer. The patient rates the level of distress over the past week.

assessed by the presence of comorbidity (yes/no)

assessed by the presence of family history of first-degree relative's breast cancer and presence of mutation (yes/no)

assessed by one of the following categories: Premenopausal Perimenopausal Postmenopausal

assessed by indicating the following clinical characteristics: pT (mm) pN (N1,N2,N3) Histological type (ductal, lobular, other) ER (%) PR (%) Grade (I, II,III) HER2 positive or negative (FISH, SISH/CISH)

assessed using one of the following categories: quadrantectomy or mastectomy, axillary dissection or sentinel lymph node biopsy

assessed by indicating the type of oncological treatment using one of the following categories: chemotherapy, radiotherapy, endocrine therapy or Anti HER2 therapy

assessed by indicating the type of the Psychotropic medication prescribed to the patient

assessed by the following Laboratory tests: hemoglobin (gdl), leukocytes (10\*3 mcl), thrombocytes (10\*3 mcl), neutrophils (%), hs-CRP(number)

Assessed by the highest level of education indicated by patients using one of the following categories: Primary school, Secondary school, High school, Vocational non-academic diploma, Bachelor degree, Postgraduate education (Master degree, PhD, Nba, specialization, etc.,)

assessed by the type of civil status indicated by patients using one of the following categories: Single, Engaged, Common-law partner, Married, Separated/divorced, Widowed

assessed by the number of children declared by patients

assessed by occupational status indicating by patients using one of the following categories: Employed full time, Employed part time, Self-employed, Unemployed, Retired, Housewife

assessed by the number of workdays missed because of illness or treatments declared by patients

assessed by asking patients if they had support from employer to obtain more flexible work arrangements

assessed by asking patients if they returned to work

assessed by asking patients to indicate monthly income using one of the following categories: 0-500; 501-1,000; 1,001-1,500; 1,501-2,000; 2,001-2,500; 2,501-3,000; 3,001-3,500; 3,501-4,000; 4,001-4,500; 4,501 and up

assessed by asking patients to describe religion faith using the following categories: Ateist, Practicing believer, Non-practicing believer,

assessed by asking patients if they met mental health professional

assessed by asking patients if their partner or someone in their family reduced their work time to take care of the patient

assessed by asking patients to indicate if they are engaged in some activities to support well-being (e.g. culture\&arts, other hobbies, mindfulness/yoga, etc)

assessed by number of days declared by patients

assessed by the number of smoked cigarettes

assessed by asking patients how often they drink alcohol using one of the following categories: Never, Less than once a month, Once or twice a month, About once a week, Several times a week, Every day

assessed by asking patients if they use drugs and the type of drugs using one of the following categories: No, Not medically-prescribed drugs (such as tranquilizers, Ritalin or strong pain-killers), Medically-prescribed cannabis, Not medically-prescribed cannabis, Other drugs (such as MDMA or cocaine)

assessed by asking patients if they followed a specific diet and what type of diet, using one of the following categories: Low-calories, Low-carb, Mediterranean, Protein-only, Vegan, Vegeterian, Gluten-free, Dairy-free, FODMAP-free, Macrobiotic diet, Other, please specify

assessed by asking patients the type and the amount of physical excercise using one of the following categories: Moderate aerobic exercise (for example walking, cycling) in min/week; Heavy aerobic exercise (for example running, HIIT training) in min/week; Muscle training in times/week; no exercise.

Helsinki University Central Hospital

Hebrew University of Jerusalem

Fundacao Champalimaud

Foundation for Research and Technology - Hellas

Institute of Communications and Computer Systems, Athens, Greece

SINGULARLOGIC

NHG CONSULTING OY

Noona Healthcare

European Institute of Oncology

Respiratory Tract (Lung and Bronchial) Diseases

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

* Fresh frozen tumor tissue and normal tissue;
* Recording of clinical characteristics, imaging, surgery features.

After treatment: FU at 2-3 weeks post surgery, 3,6,12,24 and 36 months post-surgery

(including stereotactic radiotherapy)

* Before start RT (during staging):Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
* Day 0 (before start RT): Recording of clinical characteristics, imaging, and radiotherapy features;
* Day 8-12 (during RT): Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day -30 (before start CT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, and chemotherapy features.
* Day 0 (before start RT):

  * Recording of clinical characteristics, imaging, and radiotherapy features.
  * Scoring of toxicity.
* Day 8-12 (during RT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day -30 until-18 (before start CT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, and chemotherapy features.
* Day 0 (before start RT):

  * Recording of clinical characteristics, imaging, and radiotherapy features.
  * Scoring of toxicity.
* Day 8-12 (during RT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day 0 (before start CRT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, chemotherapy features, and radiotherapy features.
* Day 8-12 (during CRT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

Day 0:

* Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
* Recording of clinical characteristics, imaging, surgery or any systemic (MoAb) features.

After treatment: FU at 2-3 weeks post treatment, 3,6,12,24 and 36 months post-treatment

Aim of the study: The main aim is to collect data of patients with lung cancer, and to perform different analyses on this data. The data contains information on patient and tumor characteristics, imaging, and treatment characteristics. With this data it is possible to improve and validate the predictive model for survival and long term toxicity in lung cancer by multicentric prospective data collection. The long term aim, beyond this specific study, is to build a Decision Support System based on the predictive models validated in this study.

Hypothesis: The general hypothesis is that we get a better prediction in terms of AUC (area under the curve) of survival and long term toxicity when we combine multifactorial variables. These variables consist of information from clinical data, imaging data, data related to treatment type and treatment quality.

1. Treatment of lung cancer

   Lung cancer is the most common cause of cancer death in The Netherlands, with an annual incidence exceeding 8,000. Two main variants of lung cancer can be identified: small cell and non-small cell lung cancer (SCLC and NSCLC respectively), the latter comprising approximately 80% of the lung cancer cases. Despite treatment improvement, the prognosis of NSCLC remains poor, with a median survival of 8 months after diagnosis, and a 5 year survival of less than 13%.

   Radiotherapy plays a key role in the treatment of NSCLC. Over the years, radiotherapeutical treatment options have increased tremendously. These include dose escalation, more intensive schedules and concurrent chemo-radiotherapy. These schedules have improved both local control and survival in patients. However, they also induce more toxicity, and the radiation oncologist faces the challenging task of choosing the optimal therapy for each patient: taking into account tumor characteristics as well as the patient's condition. In other words: the physician must estimate the expected therapeutic ratio often on a background of insufficient outcomes information.

   The same problem arises in other therapies for lung cancer, chemotherapy and surgery. In early disease, surgery is the mainstay of the treatment of lung cancer patients. This can be combined with neoadjuvant or adjuvant chemotherapy and/or radiotherapy.
2. Prediction of response

   A major problem in lung cancer management is the lack of data dealing with predictive factors for prognosis and treatment outcome. The currently used staging system (TNM) does not accurately predict outcome within homogeneous treatment groups. As a result, an individualized therapeutic ratio cannot be calculated, leading to either over- or under-treatment of many patients and hampering further optimization of any therapy.

   Attempts were undertaken to refine and improve the risk stratification, leading to the development of several prediction models. The performance of the models is usually expressed as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maximum value of the AUC is 1.0; indicating a perfect prediction model. A value of 0.5 indicates that patients are correctly classified in 50% of the cases, e.g. as good as chance.

   While high prediction accuracy (AUC=0.85) has been achieved for a population of NSCLC patients of all stages, treated with different modalities, it is a more challenging task to predict survival accurately when focusing on a subgroup. A pretreatment prediction model for patients treated with surgery yielded an AUC of 0.61 while a pretreatment model for patients treated with radiotherapy resulted in an AUC of 0.75. Further improvement of the radiotherapy model was obtained by adding information about blood biomarkers and this extended model yielded an AUC of 0.83. Investigating other blood biomarkers or possible combinations of biomarkers is a challenge and our results underline the importance of using these data in addition to clinical and imaging parameters.

   Survival remains certainly an outcome of major importance, but the last decades other treatment-related outcome measures, such as radiation induced lung injury or esophageal damage, became more important for the evaluation of treatment results.

   Pneumonitis or radiation induced lung injury has been subject of many studies. However, results are quite difficult to interpret, because many different variables, dosimetric parameters as well as other treatment or patient related characteristics, have been identified, studies showed inconsistent or even conflicting results, and sample sizes were often very limited.

   Recently, Chen et al. published a neural network model for prediction of grade 2 or higher pneumonitis, which yielded an AUC of 0.74 in the test dataset31. Compared to other models, these results are promising, although external validation of the model is warranted before it can be used in clinical practice. Our group developed a model predicting dyspnea ≥ grade 2 according to CTCv3.0. Patient as well as dosimetric parameters were incorporated in the model, which resulted in a cross-validated AUC of 0.62.

   In summary, existing models perform rather well, but there is a lot of room for improvement by adding new factors as well as applying advanced model building techniques. Prediction models still have to be developed for a number of clinically relevant outcomes. Finally, incorporating confidence intervals in the prediction as well as quantifying the gain in prediction precision if a certain diagnostic/ prognostic test is performed, would certainly be of great value for clinical use of the models (http://www.predictcancer.org/).
3. Strategies to improve prediction models for lung cancer

In order to improve the prediction models for survival as well as toxicity outcome one can include many variables as possible predictors including imaging, genomics and proteomics information.

3.1 Imaging

An important feature for prognosis on the FDG-PET-scan is the maximal Standardized Uptake Value (SUVmax). There is a statistically significant difference in 2-year survival between patients with a high pretreatment SUV and a low pretreatment SUV. Patients with a low SUVmax had a 2-year survival of 90.6%, while patients with a high SUVmax had a 2-year survival of only 58.6%. There is a significant correlation between high SUVmax and a high HIF1α staining in the biopsies, which is a marker for hypoxia. Non significant relations were shown for CA IX, Ki67 and Glut-1 and SUVmax.

Besides FDG, new PET-tracers are being developed. One of the new tracers is HX4, which is a hypoxia tracer. Regulation of tissue oxygen homeostasis is critical for cell function, proliferation and survival. Evidence for this continues to accumulate along with our understanding of the complex oxygen-sensing pathways present within cells. The microenvironment of tumors in particular is very oxygen heterogeneous, with hypoxic areas, which may explain much of our difficulty in treating cancer effectively. This is true when comparing levels of hypoxia among different patient tumors, but also within individual tumors. Accumulating evidence implicates the biological responses to hypoxia and the alterations in these pathways in cancer as important contributors to overall malignancy and treatment efficacy. This has recently prompted several investigations into the possibility of imaging and targeting treatment at the biological responses to hypoxia.

3.2 Gene signatures

Analysis of gene signatures can help to improve the predictive value of the model. An example of this, is the proliferation signature investigated by Starmans et al. Two different signatures of 110 genes were compared in prognostic value. Both showed a very good prognostic value on breast cancer data sets. The AUC (area under the curve) improved when the proliferation signature were added to the models of clinical factors. Another gene profile was tested on early stage NSCLC. This profile consists of 72 genes and is validated on stage I and II NSCLC patients of five centers. It was possible to identify early-stage NSCLC patients with high and low risk for disease recurrence and death within 3 years after primary surgical treatment.

3.3 Tumor biopsies

Hypoxia is (besides in serum) also measurable in the tissue itself. Several markers of hypoxia are predictive for survival. An example is HIF1α, which is upregulated is case of hypoxia. A higher staining of HIF1α is correlated with a worse prognosis in NSCLC. CA IX correlated with severe and chronic hypoxia, and has a strong association with a poor outcome in NSCLC.

Another marker is Ki67, which is expressed in proliferating cells. A higher Ki67 indicates more proliferation, and in a systemic review of Martin et. al. a worse prognosis was shown when Ki67 expression is increased.

3.4 Application of machine learning techniques

The availability of genomic data, together with improved imaging modalities, leads to unprecedented amounts of biological and medical data, which can only be dealt with using computational methods, not only for storing the data, but also for integrating, analyzing, displaying and eventually understanding it.

Machine learning offers a number of techniques for these purposes. These techniques can overcome problems encountered with conventional statistical methods especially if data is highly correlated, many variables are available but a limited number of patients (high-dimensional data), or many different models have to be tested for their predictive value. In the field of radiotherapy and especially for the prediction of treatment responses, machine learning is an upcoming modality. Successes over traditional statistics have already been published 43and first promising results for building predictive models concerning survival of non-small-cell lung-cancer are already found in the literature.

Radiomics: a Study of Outcome in Lung Cancer

Radiomics: a Prospective Study of Outcome in Lung Cancer

H. Lee Moffitt Cancer Center and Research Institute

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Maastricht Radiation Oncology

Nervous System Diseases

Rare Diseases

Brain Neoplasms

Central Nervous System Neoplasms

Nervous System Neoplasms

Brain Diseases

Central Nervous System Diseases

Brain Tumor, Adult

Perioperative data of patient undergoing elective brain tumor resection without requirements of red cell concentrate transfusion.

Perioperative data of patient undergoing elective brain tumor resection with requirements of red cell concentrate transfusion.

Red cell concentrate transfusion

Martinez-Simon Antonio, PhD, MD

To validate a predictive model for the risk of receiving RBCs in this population. This model uses four preoperative values (haemoglobin levels, tumour volume, previous craniotomy in the same spot, and number of craniotomies foreseen). The investigators would like to create an online data collection tool and calculator.

Observational study collecting data from different institutions and teams groups in Spain. The investigators aim at validating one model (published in Journal of Neurosurgical Anesthesiology) in different settings and populations to see if it can be accurately used to provide an opportunity to optimize both the patient and the surgical plan as well as to guide perioperative crossmatch and RBC orders.

Inclusion criteria - Brain tumour (primary or metastatic) patients undergoing craniotomy for resection.

Exclusion criteria Patients younger than 18 years old. Urgent/emergent surgery. Brain tumours not requiring full craniotomy. Patients with blood conditions causing coagulation abnormalities. Systemic disease associated with significant abnormalities in coagulation times (prothrombin time \<60% or activated thromboplastin time \> 50 sg).

Outcome The investigators aim at developing objective and simple criteria about the probability of requirements RBCs during a brain tumour resection surgery. This way we could create a simple tool that would help to optimize both the patient and the surgical plan as well as to guide perioperative crossmatch and RBCs orders.

Statistical Analysis

Transfusion requirement predictive probability will be calculated as follows:

〖1/(1+e〗\^(-(5.7606 + \[-0.7037\*(PH in g/dL)+ 0.0103\*(BTLTV in mm3)+ 1.0558\*(NC)+ 0.6232\*(PC)\]))) PH: Preoperative haemoglobin; BTLTV: brain tumour lesions total volume; NC: craniotomies number; PC: previous craniotomy. NT=1 if NT\>1, or 0; PC=1 if PC at the same site for resection (not only biopsy), or 0. Model discrimination and calibration will be assessed with area under ROC curve, and Hosmer-Lemeshow test with calibration plot, respectively. P\<0.05 will indicate statistical significance.

Predictive Model to Calculate the Risk of RBC Transfusion in Elective Brain Tumours Resections (TScoreBTR)

Validation of a Predictive Model to Calculate the Risk of Red Blood Cells Concentrates Transfusion in Patients Undergoing Elective Brain Tumours Resections

Number of red cell concentrate transfused

Brain tumor volume (mm3) on preoperative MRI

Number of patients with previous tumor craniotomy at the same place

Hospital Clinic of Barcelona

University of Salamanca

Hospital de Leon

Parc Taulí Hospital Universitari

Hospital de Cruces

Hospital del Mar

Hospital Universitario Marqués de Valdecilla

Hospital Universitario Central de Asturias

Hospital Son Espases

Hospital Clínico Universitario Virgen de la Arrixaca

Hospital General Universitario de Valencia

Hospital Universitari de Bellvitge

Hospital Miguel Servet

Clinica Universidad de Navarra, Universidad de Navarra

At time of previously scheduled clinical breast MRI, 60 second postcontrast imaging sequence will be added to the clinical breast MRI. MRI will be interpreted according to usual departmental protocols.

Anonymized, post-processed MRI imaging data will be analyzed to construct a model predicting benign vs malignant lesions. Model will be tested on a second cohort.

Predictive Model

Anonymized research sequence with the addition of modeling data (Aims 1A and 1B) will be read by three blinded breast imagers and final evaluation compared to the full protocol.

Breast Imagers

Laura Heacock, MD

The purpose of this study is to use a magnetic resonance imaging (MRI) sequence called golden-angle radial compressed sensing and parallel imaging (GRASP) as a noninvasive and fast way to measure the wash-in contrast (dye) in the breast. MRI uses a strong magnetic field and radio waves to create images of structures in the body. The goal is to determine if benign and malignant breast lesions have different patterns of contrast wash in.

This study will ask woman planning to undergo a clinical breast MRI with contrast (dye) as part of their standard care.

Abbreviated Breast MRI (AB-MRI) With Golden-angle Radial Compressed-sensing and Parallel Imaging (GRASP)

Abbreviated Breast MRI (AB-MRI) With Golden-angle Radial Compressed-sensing and Parallel Imaging (GRASP): a Short, Comprehensive Breast MRI Exam Ready for Clinical Prime Time.

The de-identified participant data from the final research dataset used in the published manuscript will be shared upon reasonable request beginning 9 months and ending 36 months following article publication or as required by a condition of awards and agreements supporting the research provided the investigator who proposes to use the data executes a data use agreement with NYU Langone Health. Requests may be directed to the PI. The protocol and statistical analysis plan will be made available on Clinicaltrials.gov only as required by federal regulation or as a condition of awards and agreements supporting the research.

NYU Langone Health

Carcinoma, Non-Small-Cell Lung

Multiple Pulmonary Nodules

Patients with pulmonary nodule diagnosed as malignant cancer by pathological examinations after surgical resection.

Patients with pulmonary nodule diagnosed as benign disease by pathological examinations after surgical resection.

Pathological diagnosis, as the golden standard for diagnosing pulmonary nodules, is achieved by two experienced pathologists and validated by the third senior pathologist.

Pathological examinations as the golden diagnosis criteria

Exhaled breath of each participant will be collected with Tedlar bags. The volatile organic compounds will be detected by a high-resolution high-pressure photon ionization time-of-ﬂight mass spectrometry (HPPI-TOFMS).

The Imaging markers of pulmonary nodules, including the size, three-dimensional shape, and appearance characteristics, are derived from the chest computed tomography within 2 weeks before surgery resection.

Breath test combined with computed tomography

Mantang Qiu, PhD

Peiyu Wang, MM

Pulmonary nodules diagnosis using breath test of volatile organic compound (VOC) is in its infancy. The accuracy of VOC analysis in diagnosing malignant pulmonary nodules varies cross the published studies. The diagnosis accuracy of VOC alone is generally poor. We speculate that the accuracy of diagnosing malignant pulmonary nodules will be improved by combining breath test with chest computed tomography (CT). This study aims to establish a predictive model of malignant pulmonary nodule using bio-markers from exhaled breath and image-markers from chest CT with retrospective data from multi centers. The sensitivity, specificity and accuracy of the model will be validated prospectively.

Endogenous volatile organic compounds (VOCs) can be derived from many different metabolic pathways. VOCs can be transported to the alveoli through the blood circulation and expelled by exhalation. Changes in VOCs production, clearance, and alterations in lung air-blood exchange functions can lead to aberrant VOCs profiles in the exhaled breath. Testing exhaled breath has the advantages of being completely non-invasive and easy to collect, and has been considered as a perfect approach for disease diagnoses and therapeutic monitoring. Many clinical studies have found that VOCs in exhaled breath are closely related to disease status. Specific VOCs alterations have been identified in many tumors, especially lung cancer. Pulmonary nodules diagnosis using breath test of volatile organic compound (VOC) is in its infancy. The accuracy of VOC analysis in diagnosing malignant pulmonary nodules varies cross the published studies. The diagnosis accuracy of VOC alone is generally poor. We speculate that the accuracy of diagnosing malignant pulmonary nodules will be improved by combining VOC analysis with chest computed tomography.

In this study, we use a highly sensitive mass spectrometry to detect exhaled VOCs of patients with pulmonary nodules. The chest CT will be used for detecting the imaging characteristics of pulmonary nodules. The pathological diagnosis of pulmonary nodules after surgical resections is selected as golden standard.

This study aims to establish a predictive model of malignant pulmonary nodule using bio-markers from breath test and image-markers from chest CT with retrospective data from multi centers. The sensitivity, specificity and accuracy of the model will be varied prospectively.

Volatile organic compound (VOC) in exhalation collected with air bags.

Breath Combined With CT for Diagnoses of Pulmonary Nodules

Assessment of the Efficacy of Breath Test Combined With Computed Tomography for Diagnoses of Pulmonary Nodules.

Using pathological diagnosis of pulmonary nodule as gold standard, diagnostic specificity of breath test combined with computed tomography will be calculated

Using pathological diagnosis of pulmonary nodule as gold standard, diagnostic accuracy of breath test combined with computed tomography will be calculated

Using pathological diagnosis of pulmonary nodule as gold standard, positive predictive value of breath test combined with computed tomography will be calculated

Using pathological diagnosis of pulmonary nodule as gold standard, negative predictive value of breath test combined with computed tomography will be calculated

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