Differential clinical characteristics, treatment response and prognosis of locally advanced adenocarcinoma/adenosquamous carcinoma and squamous cell carcinoma of cervix treated with definitive radiotherapy

2014 ◽  
Vol 93 (7) ◽  
pp. 661-668 ◽  
Author(s):  
Jenny Ling-Yu Chen ◽  
Chao-Yuan Huang ◽  
Yu-Sen Huang ◽  
Ruey-Jien Chen ◽  
Chun-Wei Wang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Treatment Response ◽  
Squamous Cell ◽  
Clinical Characteristics ◽  
Adenosquamous Carcinoma ◽  
Locally Advanced ◽  
Definitive Radiotherapy ◽  
Carcinoma Of Cervix

Outcome analysis of locally advanced adenocarcinoma/adenosquamous carcinoma and squamous cell carcinoma of cervix after definitive radiotherapy.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e16503-e16503
Author(s):  
Jenny Ling-Yu Chen ◽  
Chao-Yuan Huang ◽  
Sung Hsin Kuo ◽  
Jason C. Cheng
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Treatment Response ◽  
Squamous Cell ◽  
Adenosquamous Carcinoma ◽  
Locally Advanced ◽  
Figo Stage ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Complete Treatment

e16503 Background: To compare tumor characteristics, treatment response, failure patterns, and clinical outcome of patients with cervix locally advanced adenocarcinoma (AC)/adenosquamous carcinoma (ASC) and squamous cell carcinoma (SCC). Methods: Medical records for 36 patients with stage IIB–IVA histologically proven cervix AC/ASC and 199 patients with SCC who received definitive radiotherapy (RT) or concurrent chemoradiotherapy (CCRT) between 1995 and 2009 were retrospectively reviewed. The clinicopathologic features, radiation dose, treatment response, recurrent pattern, metastases pattern, and survival outcome between histologic groups were evaluated. Results: Compared with the SCC subgroup of patients, patients with AC/ASC were significantly younger (mean age, 56 vs. 62 years, p=0.007), more of them without clinical symptoms were diagnosed by abnormal Papanicolaou smear findings (16.7% vs. 5.5%, p = 0.019), and less responded to treatment (complete response, 72.2% vs. 86.9%, p = 0.024). After a median follow-up of 59.3 months, patients with AC/ASC had worse 5-year recurrence-free survival (RFS) (33.7% vs. 58.7%, p = 0.046), worse 5-year distant metastasis-free survival (40.2% vs. 70.3%, p = 0.002), and trends toward worse 5-year local recurrence-free survival (62.4% vs. 75.7%, p = 0.127) and worse 5-year overall survival (OS) (40.1% vs. 58.0%, p = 0.062) than those with SCC. In univariate analysis, early FIGO stage and complete treatment response were found to be significantly associated with both RFS and OS. Histology of non-AC/ASC and Point A biologically equivalent doses in 2-Gy fractions >85Gy were significantly associated with better RFS, and CCRT was significantly associated with better OS. In multivariate analysis, complete treatment response remained significant factor for predicting better RFS, and early FIGO stage, CCRT and complete treatment response remained significant factors for predicting better OS. Conclusions: Cervix AC/ASC may behave more aggressively than does SCC. For this subgroup of cervical cancer, more comprehensively effective treatment is warranted.

scholarly journals Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Konstantin Hellwig ◽  
Stephan Ellmann ◽  
Markus Eckstein ◽  
Marco Wiesmueller ◽  
Sandra Rutzner ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Treatment Response ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Pathologic Complete Response ◽  
Residual Tumor ◽  
Multiparametric Mri ◽  
Neck Squamous Cell Carcinoma

ObjectivesTo assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.MethodsTwenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.ResultsFifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).ConclusionsDCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

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