Survival prediction in patients treated by FOLFIRI and bevacizumab for metastatic colorectal cancer (PRODIGE 9) using contrast-enhanced CT texture analysis (SPECTRA).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 3601-3601 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Predictive Factors ◽  
Liver Lesion ◽  
Rank Test ◽  
Survival Prediction ◽  
Survival Status ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria ◽  
Response To Chemotherapy ◽  
Patient Will ◽  
Radiomics Signature

3601 Background: Quantitative assessment of tumor architecture changes may help to early identify non-responder patients and propose a tailored treatment strategy. Our objective was to build and validate a radiomics signature able to predict early the lack of response to chemotherapy including FOLFIFRI and bevacizumab using baseline and first evaluation CT and to compare it to the RECIST and morphological criteria. Methods: For 230 patients of PRODIGE 9 study and treated by FOLFIRI and bevacizumab, a computed analysis (CA) was performed on the dominant liver lesion (DLL) at baseline and 2 months post-chemotherapy. RECIST evaluation was performed at 2 and 6 months. The sum of the target liver lesions (STL), the density of the DLL, CA parameters and their changes rates were correlated with the 2-year survival status. A radiomics signature combining 3 parameters was built in one arm and validated in the second arm. Survival was estimated with the Kaplan-Meier method and compared with log-rank test. Results: The strongest predictive factors for 2-year survival status were decrease in STL(AUC = .69±.05[95%CI:.60-.77]), change rate in kurtosis(ssf = 0) (AUC = .66±.05[95%CI:.57-.74]), and the baseline density of the DLL (AUC = .68±.05[95%CI:.59-.77]). Using multivariate analysis, predictive factors of 2-year survival status were the decrease in STL > 15%(HR = 1.92, P= .002), the increase in kurtosis value(ssf = 0) > 93% (HR = 2.16, P= .001), and baseline DLL > 64.3UH (HR = 1.70, P= .02). Then, the SPECTRA-score was built by according 1 point for each of the 3 criteria. Patients with a SPECTRA-score > 1 had a lower overall survival in the training ( P= .001) and in the validation cohort ( P= .002). Non-response according to RECIST at 6 months had the same prognostic value as SPECTRA-score>1 at 2 months. Conclusions: A radiomics signature combining STL, density and CA on baseline and first evaluation CT is be able to predict which patient will have a poor outcome with same performances than standard evaluation with RECIST1.1 at 6 months in mCRC patients. Clinical trial information: NCT00952029.

An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer

2021 ◽  
Author(s):  
Ru-ru Zheng ◽  
Meng-ting Cai ◽  
Li Lan ◽  
Xiao Wan Huang ◽  
Yun Jun Yang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Early Stage ◽  
Disease Free Survival ◽  
Clinicopathological Parameters ◽  
Rank Test ◽  
Survival Prediction ◽  
Early Stage Cervical Cancer ◽  
Radiomics Signature

Objectives: To investigate the prognostic role of Magnetic Resonance Imaging (MRI) based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer. Methods: A total of 207 cervical cancer patients (training cohort: n = 144; validation cohort: n = 63) were enrolled. 792 radiomics features were extracted from T2-weighted (T2W) and diffusion weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps. Results: A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS (p < 0.05) and DFS (p < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3 year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics. Conclusions: The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application. Advances in knowledge: This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.

Development and validation of a nomogram for distant metastasis in esophageal cancer based on radiomics and clinical factors.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16071-e16071
Author(s):  
Zhu Chao ◽  
Qingtao Qiu ◽  
Youxin Ji ◽  
Songping Wang ◽  
Jialin Ding ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Predictive Factors ◽  
Distant Metastasis ◽  
Machine Learning Algorithms ◽  
Lasso Regression ◽  
Clinical Factors ◽  
Net Reclassification Improvement ◽  
Contrast Enhanced Ct ◽  
Development And Validation ◽  
Radiomics Signature

e16071 Background: Distant metastasis with an incidence of 25% in esophageal cancer(EC) represents a poor prognosis. However, there was few study for prediction of distant metastasis in EC, due to unsatisfactory specificity of clinical factors and lack of reliable biomarkers. Methods: Two hundred and ninety-nine patients were enrolled and randomly assigned to a training cohort(n = 207) and a validation cohort(n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictive factors and construct a clinical nomogram. Radiomic features were extracted from contrast-enhanced CT performed before treatment, and Lasso regression was used to screen the optimal features, which were developed a radiomics signature subsequently. Four machine learning algorithms were used to establish radiomics models respectively based on the screened features. The joint nomogram incorporating radiomics signature and clinical independent predictors was developed by logical regression algorithm. All models were further validated by discrimination,caliberation, reclassification and clinical usefulness. Results: The joint nomogram had a better performance [AUC(95%CI): 0.827(0.742-0.912)] than clinical nomogram [AUC(95%CI): 0.731(0.626-0.836)]and radiomics predictive models[AUC(95%CI): 0.747(0.642-0.851),SVM algorithms]. Caliberation curve, and decision curve analysis also revealed joint nomogram outperformed the other models. Compared with the clinical nomogram, net reclassification Improvement(NRI) of the joint nomogram was improved by 0.114(0.075, 0.345),and integrated discrimination Improvement (IDI) was improved by 0.071(0.030-0.112), P= 0.001. Conclusions: We constructed and validated the first joint nomogram for distant metastasis in EC based on radiomics signature and clinical independent predictive factors, which could help clinicians to identify patients with high risk of distant metastasis.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Hepatic Metastases ◽  
Survival Prediction ◽  
Training Cohort ◽  
Link Type ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria

PurposeThe objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.MethodsThis study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.ResultsIn the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10–7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).ConclusionA radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.Trial registrationClinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

scholarly journals Interesting case discussion of a lesion over the right abdominal wall

2019 ◽  
Vol 6 (2) ◽  
pp. 636
Author(s):  
Anand Ignatius Peter ◽  
Souvik Patra ◽  
Samreen Jaffar
Keyword(s):  
Pleural Effusion ◽  
Abdominal Wall ◽  
Malignant Lesion ◽  
Skin Surface ◽  
Liver Lesion ◽  
Interesting Case ◽  
Lumbar Region ◽  
Contrast Enhanced Ct ◽  
Surgical History ◽  
The Right

A diagnosis of hepatic actinomycosis is challenging and often overlooked because of its indiscernible nature and slow rate of progression. This is further complicated with absence of any specific clinical and radiologic manifestations. In this case, a 49 years old male, farmer, with no co-morbidities or significant past medical or surgical history, reported to the department of surgery, with complains of non-healing ulcer over right upper abdomen since five months. Examination of the ulcer led to a clinical suspicion of a malignant lesion. Sonogram of abdomen and pelvic region, which revealed heterogenous lesion with central necrosis in the right lumbar region of the abdominal wall with extension into the skin surface, a heterogenous lesion noted on the liver, and right pleural effusion with the suggestion to consider the possibility of primary skin/abdominal wall tumor with hepatic metastasis with right pleural effusion. Further investigation was performed using contrast enhanced CT scan which also favoured the diagnosis of a malignancy. However, biopsy of the skin lesion was negative for malignancy and it confirmed the diagnosis of Actinomycosis. This diagnosis was further confirmed with an ultrasound guided biopsy of the liver lesion. The patient was then started on appropriate treatment for the same and he recovered well.

scholarly journals Survival Status and Predictors of Mortality among Newborns Admitted with Neonatal Sepsis at Public Hospitals in Ethiopia

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Samuel Dessu ◽  
Aklilu Habte ◽  
Tamirat Melis ◽  
Mesfin Gebremedhin
Keyword(s):  
Neonatal Sepsis ◽  
Late Onset ◽  
Survival Curve ◽  
Multivariable Analysis ◽  
Public Hospitals ◽  
Rank Test ◽  
Cumulative Proportion ◽  
Predictors Of Mortality ◽  
Survival Status ◽  
History Of

Background. One-fourth of neonatal death is due to neonatal sepsis and nearly 98% of these deaths are occurring at low- and middle-income countries. In Ethiopia, forty percent of under-five mortality occurs during the neonatal period, of which neonatal sepsis accounts for 30-35% of neonatal deaths next to prematurity and its complications. On the other side, among the survived neonates with neonatal sepsis, there exist as vulnerable to short and long-term neurological and developmental morbidity impacting the overall productivity of the child as adult. Methods. A longitudinal prospective cohort study was conducted among selected 289 neonates with neonatal sepsis who were admitted in the neonatal intensive care unit at public hospitals in Ethiopia from 1st March 2018 to 31st December 2019. Data were entered into Epi data version 3.02 and exported to SPSS V 25 for analysis. The Kaplan-Meier survival curve together with log-rank test was used to estimate the survival time of the neonates. Variables which had p value < 0.05 in multivariable analysis using the cox proportional hazard model were declared as statistically significant predictors of mortality. Results. The study was conducted with a total of 289 neonates admitted with neonatal sepsis. The cumulative proportion of surviving at the end of the fourth day was 99.5%, and it was 98.2% at the end of the fifth day. In addition, it was 96.6%, 93.5%, and 91.1% at the end of the sixth, seventh, and eighth day, respectively. The incidence of mortality was 8.65 per 100 neonates admitted with neonatal sepsis. Having a history of intrapartum fever (AHR: 14.5; 95% CI: 4.25, 49.5), history of chorioamnionitis (AHR: 5.7; 95% CI: 2.29, 13.98), induced labor (AHR: 7; 95% CI: 2.32, 21.08), and not initiating exclusive breastfeeding within one hour (AHR: 3.4; 95% CI: 1.34, 12.63) were the independent predictors of mortality. Conclusion. The survival status of neonates among neonates admitted with neonatal sepsis was high at the early admission days and high cumulative proportion of death as the admission period increased. The risk of mortality was high among the neonates with early onset of neonatal sepsis as compared with late onset of neonatal sepsis and history of intrapartum fever, history of diagnosed chorioamnionitis, onset of labor, and EBF initiation within one hour were the independent predictors of mortality among neonates admitted with neonatal sepsis.

scholarly journals Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Biomolecules ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1460 ◽  
Author(s):  
Satoshi Takahashi ◽  
Ken Asada ◽  
Ken Takasawa ◽  
Ryo Shimoyama ◽  
Akira Sakai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Array ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Survival Prediction ◽  
Reverse Phase Protein Array ◽  
Reverse Phase ◽  
Log Rank Test ◽  
Omics Analysis ◽  
Phase Protein

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

scholarly journals Long-term outcomes in osteosarcoma patients in the Groote Schuur Hospital patient population: A retrospective review

2017 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Heide Hart ◽  
Jeannette D. Parkes
Keyword(s):  
Predictive Factors ◽  
Retrospective Review ◽  
Patient Population ◽  
Univariate Analysis ◽  
Disease Free Survival ◽  
Long Term Outcomes ◽  
Long Term Outcome ◽  
Primary Disease ◽  
Response To Chemotherapy

<strong>Background:</strong> Predictive factors for long-term outcomes in osteosarcoma patients are still controversial. There is no literature available regarding these factors in a patient population in a developing country.<br /><strong>Aim and setting:</strong> To determine the outcome of treatment of osteosarcoma patients treated at Groote Schuur Hospital from 1990 to 2012 in terms of locoregional control (LRC), disease-free survival (DFS) and overall survival (OS) and to determine the value of suggested predictive factors in this population.<br /><strong>Patients and methods:</strong> Retrospective review of all patients diagnosed with and treated for osteosarcoma at Groote Schuur Hospital between 1990 and 2012, considering OS, DFS and LRC. This review assesses the significance of suggested predictive factors from other studies, namely, HIV status, age at diagnosis, site of primary disease, type of chemotherapy used, response to chemotherapy and type of surgery in terms of OS, DFS and LRC.<br /><strong>Results:</strong> Forty-three patients with histologically confirmed osteosarcoma were treated at Groote Schuur Hospital between 1990 and 2012. Median 5 year OS was 57.8%. On univariate analysis, the site of disease was the only statistically significant predictive factor for prognosis.<br /><strong>Conclusion:</strong> On univariate analysis, patients with axial disease have a worse predicted prognosis than those with primary disease in their extremities. The clinical behaviour and long-term outcome after treatment of these patients with osteosarcoma are similar to that seen internationally.

Nasopharyngeal carcinoma at the National Institute of Oncology

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e17055-e17055
Author(s):  
E. Hassan ◽  
S. Afqir ◽  
N. Ismaili ◽  
H. M’rabti ◽  
S. Boutayeb ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Disease Free Survival ◽  
Staging System ◽  
Cancer Staging ◽  
Undifferentiated Carcinoma ◽  
Rank Test ◽  
Entire Group ◽  
Female Ratio ◽  
Response To Chemotherapy

e17055 Background: To evaluate the disease characteristics and outcome of patients with nasopharyngeal carcinoma treated at the National Institute of Oncology (NIO) Rabat, Morocco. Methods: Between 1999 and 2001, 468 patients with a diagnosis of nasopharyngeal carcinoma were treated at the NIO. The median age was 42 years (range 10 to 83). The male/female ratio was 2.5/1. Of the 468 patients, 88 (19%), and 380 (81%) had T1-T2, and T3- T4 (TNM International Union Against Cancer staging system, 1997), respectively. Ninety percent presented with nodal metastasis. 163 patients (35%) had lymph nodes >6 cm, and 229 (49%) had bilateral nodes at presentation. Histologically, 405 patients (86%) had undifferentiated carcinoma. Seventy-six percent received neoadjuvant multiagent chemotherapy containing cisplatin, followed by radiotherapy (RT). Results: After a median follow-up of 26 months, the disease-free survival (DFS) and overall survival (OS) rate for the entire group was 27% and 41%, respectively. Kaplan-Meier curves were used for evaluation of prognostic factors and were compared using the log-rank test. Nodal status had a significant impact on OS (p < 0.001). Complete responders to chemotherapy had superior DFS and OS. Conclusions: Combined modality management using chemotherapy and RT resulted in satisfactory locoregional control and OS in patients with nasopharyngeal carcinoma. Nodal involvement and response to chemotherapy were important prognostic factors. No significant financial relationships to disclose.

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