Dataset Information

Adjuvant therapy sparing in rectal cancer achieving complete response after chemoradiation.

ABSTRACT:

Aim

To evaluate the long-term results of conventional chemoradiotherapy and laparoscopic mesorectal excision in rectal adenocarcinoma patients without adjuvant therapy.

Methods

Patients with biopsy-proven adenocarcinoma of the rectum staged cT3-T4 by endoscopic ultrasound or magnetic resonance imaging received neoadjuvant continuous infusion of 5-fluorouracil for five weeks and concomitant radiotherapy. Laparoscopic surgery was planned after 5-8 wk. Patients diagnosed with ypT0N0 stage cancer were not treated with adjuvant therapy according to the protocol. Patients with ypT1-2N0 or ypT3-4 or N+ were offered 5-fluorouracil-based adjuvant treatment on an individual basis. An external cohort was used as a reference for the findings.

Results

One hundred and seventy six patients were treated with induction chemoradiotherapy and 170 underwent total mesorectal excision. Cancer staging of ypT0N0 was achieved in 26/170 (15.3%) patients. After a median follow-up of 58.3 mo, patients with ypT0N0 had five-year disease-free and overall survival rates of 96% (95%CI: 77-99) and 100%, respectively. We provide evidence about the natural history of patients with localized rectal cancer achieving a complete response after preoperative chemoradiation. The inherent good prognosis of these patients will have implications for clinical trial design and care of patients.

Conclusion

Withholding adjuvant chemotherapy after complete response following standard neoadjuvant chemoradiotherapy and laparoscopic mesorectal excision might be safe within an experienced multidisciplinary team.

SUBMITTER: Garcia-Albeniz X

PROVIDER: S-EPMC4229549 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

Adjuvant therapy sparing in rectal cancer achieving complete response after chemoradiation.

García-Albéniz Xabier X Gallego Rosa R Hofheinz Ralf Dieter RD Fernández-Esparrach Gloria G Ayuso-Colella Juan Ramón JR Bombí Josep Antoni JA Conill Carles C Cuatrecasas Miriam M Delgado Salvadora S Ginés Angels A Miquel Rosa R Pagés Mario M Pineda Estela E Pereira Verónica V Sosa Aarón A Reig Oscar O Victoria Iván I Feliz Luis L María de Lacy Antonio A Castells Antoni A Burkholder Iris I Hochhaus Andreas A Maurel Joan J

World journal of gastroenterology 20141101 42

<h4>Aim</h4>To evaluate the long-term results of conventional chemoradiotherapy and laparoscopic mesorectal excision in rectal adenocarcinoma patients without adjuvant therapy.<h4>Methods</h4>Patients with biopsy-proven adenocarcinoma of the rectum staged cT3-T4 by endoscopic ultrasound or magnetic resonance imaging received neoadjuvant continuous infusion of 5-fluorouracil for five weeks and concomitant radiotherapy. Laparoscopic surgery was planned after 5-8 wk. Patients diagnosed with ypT0N0 ...[more]

PMID: 25400468

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Project description:PurposePathologic complete response (pCR) is a critical factor in determining whether patients with rectal cancer (RC) should have surgery after neoadjuvant chemoradiotherapy (nCRT). Currently, a pathologist's histological analysis of surgical specimens is necessary for a reliable assessment of pCR. Machine learning (ML) algorithms have the potential to be a non-invasive way for identifying appropriate candidates for non-operative therapy. However, these ML models' interpretability remains challenging. We propose using explainable boosting machine (EBM) to predict the pCR of RC patients following nCRT.MethodsA total of 296 features were extracted, including clinical parameters (CPs), dose-volume histogram (DVH) parameters from gross tumor volume (GTV) and organs-at-risk, and radiomics (R) and dosiomics (D) features from GTV. R and D features were subcategorized into shape (S), first-order (L1), second-order (L2), and higher-order (L3) local texture features. Multi-view analysis was employed to determine the best set of input feature categories. Boruta was used to select all-relevant features for each input dataset. ML models were trained on 180 cases from our institution, with 37 cases from RTOG 0822 clinical trial serving as the independent dataset for model validation. The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations.ResultsThe best input feature categories were CP+DVH+S+R_L1+R_L2 for all models, from which Boruta-selected features enabled the EBM, XGB, RF, and SVM models to attain the AUCs of 0.820, 0.828, 0.828, and 0.774, respectively. Although EBM did not achieve the best performance, it provided the best capability for identifying critical turning points in response scores at distinct feature values, revealing that the bladder with maximum dose >50 Gy, and the tumor with maximum2DDiameterColumn >80 mm, elongation <0.55, leastAxisLength >50 mm and lower variance of CT intensities were associated with unfavorable outcomes.ConclusionsEBM has the potential to enhance the physician's ability to evaluate an ML-based prediction of pCR and has implications for selecting patients for a "watchful waiting" strategy to RC therapy.

Dataset Information

Adjuvant therapy sparing in rectal cancer achieving complete response after chemoradiation.

Aim

Methods

Results

Conclusion

Publications

Adjuvant therapy sparing in rectal cancer achieving complete response after chemoradiation.

Similar Datasets

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