scholarly journals Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

2016 ◽  
Vol 18 (12) ◽  
pp. 1680-1687 ◽  
Author(s):  
Ken Chang ◽  
Biqi Zhang ◽  
Xiaotao Guo ◽  
Min Zong ◽  
Rifaquat Rahman ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Recurrent Glioblastoma ◽  
Machine Learning Techniques ◽  
Imaging Biomarkers ◽  
Imaging Features ◽  
Mri Features ◽  
Fluid Attenuated Inversion Recovery ◽  
Multimodal Mri

Abstract Background Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab. Methods The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model. Results Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively. Conclusion With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

Local Ablative Therapies in Oligometastatic NSCLC: New Data and New Directions

2020 ◽  
Vol 41 (03) ◽  
pp. 369-376
Author(s):  
Pencilla Lang ◽  
Daniel R. Gomez ◽  
David A. Palma
Keyword(s):  
Clinical Decision Making ◽  
Treatment Phase ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Stereotactic Ablative Radiotherapy ◽  
Free Survival ◽  
Fractionated Radiotherapy ◽  
Disease States ◽  
Ablative Therapies ◽  
Clinical Scenarios

AbstractThe oligometastatic and oligoprogressive disease states have been recently recognized as common clinical scenarios in the management of non-small cell lung cancer (NSCLC). As a result, there has been increasing interest in treating these patients with locally ablative therapies including surgery, conventionally fractionated radiotherapy, stereotactic ablative radiotherapy, and radiofrequency ablation. This article provides an overview of oligometastatic and oligoprogressive disease in the setting of NSCLC and reviews the evidence supporting ablative treatment. Phase II randomized controlled trials and retrospective series suggest that ablative treatment of oligometastases may substantially improve progression-free survival and overall survival, and additional large randomized studies testing this hypothesis in a definitive context are ongoing. However, several challenges remain, including quantifying the possible benefits of ablative therapies for oligoprogressive disease and developing prognostic and predictive models to assist in clinical decision making.

scholarly journals Clinical value of measurable residual disease testing for assessing depth, duration, and direction of response in multiple myeloma

2020 ◽  
Vol 4 (14) ◽  
pp. 3295-3301
Author(s):  
Joaquin Martinez-Lopez ◽  
Sandy W. Wong ◽  
Nina Shah ◽  
Natasha Bahri ◽  
Kaili Zhou ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Clinical Decision Making ◽  
Residual Disease ◽  
Clinical Care ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Routine Practice ◽  
Clinical Value ◽  
The Impact ◽  
Measurable Residual Disease

Abstract Few clinical studies have reported results of measurable residual disease (MRD) assessments performed as part of routine practice. Herein we present our single-institution experience assessing MRD in 234 multiple myeloma (MM) patients (newly diagnosed [NDMM = 159] and relapsed [RRMM = 75]). We describe the impact of depth, duration, and direction of response on prognosis. MRD assessments were performed by next-generation sequencing of immunoglobulin genes with a sensitivity of 10−6. Those achieving MRD negativity at 10−6, as well as 10−5, had superior median progression-free survival (PFS). In the NDMM cohort, 40% of the patients achieved MRD negativity at 10−6 and 59% at 10−5. Median PFS in the NDMM cohort was superior in those achieving MRD at 10−5 vs &lt;10−5 (PFS: 87 months vs 32 months; P &lt; .001). In the RRMM cohort, 36% achieved MRD negativity at 10−6 and 47% at 10−5. Median PFS was superior for the RRMM achieving MRD at 10−5 vs &lt;10−5 (PFS: 42 months vs 17 months; P &lt; .01). Serial MRD monitoring identified 3 categories of NDMM patients: (A) patients with ≥3 MRD 10−6 negative samples, (B) patients with detectable but continuously declining clonal numbers, and (C) patients with stable or increasing clonal number (≥1 log). PFS was superior in groups A and B vs C (median PFS not reached [NR], NR, 55 respectively; P &lt; .001). This retrospective evaluation of MRD used as part of clinical care validates MRD as an important prognostic marker in NDMM and RRMM and supports its use as an endpoint in future clinical trials as well as for clinical decision making.

scholarly journals The revolution of personalized psychiatry: will technology make it happen sooner?

2017 ◽  
Vol 48 (5) ◽  
pp. 705-713 ◽  
Author(s):  
G. Perna ◽  
M. Grassi ◽  
D. Caldirola ◽  
C. B. Nemeroff
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Time Data ◽  
New Approach ◽  
Technological Advances ◽  
Learning Techniques ◽  
Personalized Psychiatry ◽  
Smart Wearable

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Imaging biomarkers exist and they underpin clinical decision-making

2017 ◽  
Vol 14 (7) ◽  
pp. 452-452 ◽  
Author(s):  
John C. Waterton ◽  
Lisa M. McShane ◽  
James P. B. O'Connor
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Imaging Biomarkers

scholarly journals Erratum: Imaging biomarkers exist and they underpin clinical decision-making

2017 ◽  
Vol 14 (11) ◽  
pp. 694-694
Author(s):  
John C. Waterton ◽  
Lisa M. McShane ◽  
James P. B. O'Connor
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Imaging Biomarkers

scholarly journals Prospective validation of a molecular prognostication panel for clival chordoma

2019 ◽  
Vol 130 (5) ◽  
pp. 1528-1537 ◽  
Author(s):  
Georgios A. Zenonos ◽  
Juan C. Fernandez-Miranda ◽  
Debraj Mukherjee ◽  
Yue-Fang Chang ◽  
Klea Panayidou ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Clinical Decision Making ◽  
Cox Model ◽  
Wide Spectrum ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Homozygous Deletion ◽  
Ki 67 ◽  
Free Survival ◽  
Clival Chordoma

OBJECTIVEThere are currently no reliable means to predict the wide variability in behavior of clival chordoma so as to guide clinical decision-making and patient education. Furthermore, there is no method of predicting a tumor’s response to radiation therapy.METHODSA molecular prognostication panel, consisting of fluorescence in situ hybridization (FISH) of the chromosomal loci 1p36 and 9p21, as well as immunohistochemistry for Ki-67, was prospectively evaluated in 105 clival chordoma samples from November 2007 to April 2016. The results were correlated with overall progression-free survival after surgery (PFSS), as well as progression-free survival after radiotherapy (PFSR).RESULTSAlthough Ki-67 and the percentages of tumor cells with 1q25 hyperploidy, 1p36 deletions, and homozygous 9p21 deletions were all found to be predictive of PFSS and PFSR in univariate analyses, only 1p36 deletions and homozygous 9p21 deletions were shown to be independently predictive in a multivariate analysis. Using a prognostication calculator formulated by a separate multivariate Cox model, two 1p36 deletion strata (0%–15% and > 15% deleted tumor cells) and three 9p21 homozygous deletion strata (0%–3%, 4%–24%, and ≥ 25% deleted tumor cells) accounted for a range of cumulative hazard ratios of 1 to 56.1 for PFSS and 1 to 75.6 for PFSR.CONCLUSIONSHomozygous 9p21 deletions and 1p36 deletions are independent prognostic factors in clival chordoma and can account for a wide spectrum of overall PFSS and PFSR. This panel can be used to guide management after resection of clival chordomas.

scholarly journals Radiogenomics and Radiomics in Liver Cancers

Diagnostics ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Aman Saini ◽  
Ilana Breen ◽  
Yash Pershad ◽  
Sailendra Naidu ◽  
M. Knuttinen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Molecular Profile ◽  
Imaging Features ◽  
Imaging Data ◽  
Prognostic Information ◽  
Cross Sectional ◽  
Related Field ◽  
Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

Management Driven Structured Reporting in Ovarian Cancer

2019 ◽  
Vol 03 (02) ◽  
pp. 153-162
Author(s):  
Anuradha Chandramohan ◽  
Sourav Panda ◽  
Anitha Thomas ◽  
Rachel Chandy ◽  
Anjana Joel ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Structured Reporting ◽  
Imaging Features ◽  
Advanced Stage ◽  
Anatomical Structures ◽  
Radiology Reports ◽  
Team Setting

AbstractSince majority (80%) of ovarian cancer patients present at an advanced stage, imaging performed on these patients have numerous findings. The combination of multiple findings on imaging, complexity of anatomical structures which are involved in ovarian cancer, and the need to perceive certain subtle imaging features which would impact management often makes it challenging to systematically review images of these patients. Similarly, it is difficult to effectively communicate these findings in radiology reports. Structured reporting that is geared toward clinical decision-making has been an area of recognized need. An understanding of the review areas, which aid clinical decision-making in a multidisciplinary team setting at our institution led us to the proposed structured reporting template for ovarian cancer. Through this review, the authors would like to share this reporting template with examples.

Prediction of benefit from treatment beyond the first line in patients with metastatic breast cancer (MBC).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11565-e11565
Author(s):  
Marta Bonotto ◽  
Lorenzo Gerratana ◽  
Alessandro Minisini ◽  
Elena Poletto ◽  
Stefania Russo ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Statistical Significance ◽  
Metastatic Breast ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Consecutive Series ◽  
First Line ◽  
Her2 Positive ◽  
Free Survival ◽  
The Impact

e11565 Background: Despite the availability of several therapeutic options for MBC, palliative treatments beyond 1st line lack of predictive factors that could help clinical decision making. We aimed to determine which is the impact of benefit at 1stline into the benefit from subsequent therapeutic lines. Methods: We analyzed a consecutive series of 472 MBC patients treated with chemotherapy (CT) and/or endocrine therapy (ET) at the Department of Oncology of Udine, Italy, between 2004 and 2012. We evaluated Progression Free Survival at 1st (PFS1), 2nd (PFS2), 3rd (PFS3) and 4th (PFS4) line of treatment. Three distinct analyses were conducted: the first for the lines of CT, the second for the lines of ET and the third by considering both CT and ET as a line of treatment. A PFS longer than 6 months was defined as “6-month benefit". Results: Median Overall Survival was 34.5 mo (25th – 75th percentile: 14.5 – 58.8), median overall PFS1 and PFS2 was 8.9 mo and 4.3 mo respectively. Median PFS1 and PFS2 in CT lines only was 7 mo and 3.7 mo, respectively. Median PFS1 and PFS2 in ET lines only was 9.4 mo and 4.6 mo respectively. Overall, 289 patients (63.5%) presented 6-month benefit at 1st line, 128 (40.5%) at 2nd, 76 (33.8%) at 3rd and 34 (23.3%) at 4th. Not having a 6-month benefit in overall PFS1 was associated with a lack of benefit both at 2nd line (OR=0.48; p=0.0026) and at any line beyond the 1st (OR=0.39; p< 0.0001). Taking into consideration CT lines only, not having a 6-month benefit in CT PFS1 was associated with a lack of benefit both at 2nd line (OR=0.45; p=0.0072) and at any line beyond the 1st (OR=0.43; p=0.0026). A lack of benefit at the 1st ET line was not associated with further ET outcome neither in 2nd line nor in any line beyond the 1st. Stratification according to immunophenotype highlighted a statistical significance only among HER2 positive tumors (OR=0.2; p=0.0152 in 2nd line and OR=0.14; p=0.0036 beyond 1st line). Conclusions: Our results suggest that the absence of a “6-month benefit” in PFS1 predicts a lack of benefit in subsequent therapy lines, especially in HER2 positive disease. However, a lack of benefit at first line ET appears not to be detrimental to further anti-hormonal lines.

Ability of multiparametric magnetic resonance imaging (MRI) to predict prostate tumor heterogeneity on targeted biopsy.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 113-113 ◽  
Author(s):  
Samuel Gold ◽  
Jonathan Bloom ◽  
Graham R. Hale ◽  
Kareem Rayn ◽  
Sherif Mehralivand ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Lesion Size ◽  
Future Research ◽  
Imaging Features ◽  
Targeted Biopsy ◽  
Impact Risk ◽  
The Difference ◽  
Magnetic Resonance Imaging Mri ◽  
Grade 3

113 Background: Prostate cancer (PCa) can show heterogeneous histology within lesions. MRI-targeted biopsy (Tbx) of the prostate improves PCa detection, but sampling within lesions has yet to be standardized. Furthermore, Tbx results are often heterogeneous as evidenced by differing histologic grades of Tbx cores within the same lesion. This introduces potential variability in biopsy results, on which clinical decisions are made. Here we aim to characterize lesion heterogeneity and identify predictive multiparametric MRI (mpMRI) features. Methods: A cohort of men who underwent mpMRI and Tbx between 2014-2017 were selected for analysis from a prospectively maintained database. To characterize lesion heterogeneity, only men with ≥2 positive Tbx cores were included. Histologic grades were scored according to International Society of Urological Pathology (ISUP) grades. Lesion heterogeneity, reported as a heterogeneity index (HI), was calculated as the difference of the average ISUP grades of Tbx cores per lesion from the maximum sampled ISUP grade of that lesion. Statistical analyses identified associations between imaging features and lesion heterogeneity. Results: 157 lesions in 114 patients met inclusion criteria. Maximum ISUP grade ranged from 1 to 5, with a median ISUP grade of 2. Higher ISUP grades were associated with greater lesion heterogeneity, HI for ISUP grade ≥3 = 0.58±0.11 vs <3 = 0.29±0.08, p = 0.0001. In addition, increasing lesion size on mpMRI was associated with greater lesion heterogeneity, HI for ≥2cm = 0.52±0.14 vs <2cm = 0.32±0.08, p = 0.0096. Finally, higher mpMRI suspicion scores were associated with increased heterogeneity vs lower suspicion scores, p = 0.048. Conclusions: mpMRI aids in characterizing PCa lesion heterogeneity to predict variability of histologic grades on Tbx. This information can assist Tbx planning to potentially reduce risks of upgrading on final pathology. Future research will examine how lesion heterogeneity can impact risk stratification and clinical decision-making for patients and practitioners. This research was supported by the Intramural Research Program of the National Cancer Institute, NIH and NIH Medical Research Scholars Program.

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