Radiotherapy for Patients With Resected Tumor Deposit–Positive Colorectal Cancer: A Surveillance, Epidemiology, and End Results–Based Population Study

Chavali, Laxmi; Llanos, Adana A.M.; Yun, Jing‐Ping; Hill, Stephanie M; Tan, Xi; Zhang, Lanjing

doi:10.5858/arpa.2017-0099-oa

Cited by 15 publications

(10 citation statements)

References 34 publications

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“…In our previous study (32), we identified the survival benefit of postoperative RT for patients with pT3N0 disease in the high-risk subgroup. Nonetheless, it remains controversial whether all TDpositive patients should receive postoperative RT (33). In the current study, we also found that postoperative RT could only prolong the median OS of patients in the high-risk subgroup, but not in the low-risk subgroup.…”

Section: Discussionsupporting

confidence: 50%

Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

et al. 2022

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AimTumor deposits (TDs) are an aggressive hallmark of rectal cancer, but their prognostic value has not been addressed in current staging systems. This study aimed to construct and validate a prognostic nomogram for rectal cancer patients with TDs.MethodsA total of 1,388 stage III–IV rectal cancer patients who underwent radical surgical resection from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed to identify the clinical value of TDs. TD-positive rectal cancer patients in the SEER database were used as the training set to construct a prognostic model, which was validated by Fujian Cancer Hospital. Three models were constructed to predict the prognosis of rectal cancer patients with TDs, including the least absolute shrinkage and selection operator regression (LASSO, model 1), backward stepwise regression (BSR, model 2), and LASSO followed by BSR (model 3). A nomogram was established among the three models.ResultsIn the entire cohort, TD was also identified as an independent risk factor for overall survival (OS), even after adjusting for baseline factors, stage, other risk factors, treatments, and all the included variables in this study (all P < 0.05). Among patients with TDs, model 3 exhibited a higher C-index and area under the curves (AUCs) at 3, 4, and 5 years compared with the American Joint Committee on Cancer staging system both in the training and validation sets (all P < 0.05). The nomogram obtained from model 3 showed good consistency based on the calibration curves and excellent clinical applicability by the decision curve analysis curves. In addition, patients were divided into two subgroups with apparently different OS according to the current nomogram (both P < 0.05), and only patients in the high-risk subgroup were found to benefit from postoperative radiotherapy (P < 0.05).ConclusionWe identified a novel nomogram that could not only predict the prognosis of rectal cancer patients with TDs but also provide reliable evidence for clinical decision-making.

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Section: Discussionsupporting

confidence: 50%

Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

et al. 2022

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“…We obtained individual-level data from the Surveillance, Epidemiology, and End Results-18 (SEER-18) (www.seer.cancer.gov) SEER*Stat Database with Treatment Data using SEER*Stat software (Surveillance Research Program, National Cancer Institute SEER*Stat software (seer.cancer.gov/seerstat) version <8.3.6>) as we did before [13][14][15]. SEER-18 is the largest SEER database including cases from 18 states and covering near 30% of the U.S. population.…”

Section: Discussionmentioning

confidence: 99%

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Deng

Huang

Yuan

et al. 2021

Laboratory Investigation

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Most of the biomedical datasets, including those of 'omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms.Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models).We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase model's efficiency while maintaining classification accuracy.

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“…With this in mind, the authors suggest that the presence of TDs should be considered when implementing adjuvant treatment regimens and protocols. Chavali et al27 suggest that adjuvant radiotherapy improves prognosis for patients with TDs who did not receive neoadjuvant radiotherapy.…”

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confidence: 99%

Prognostic value of tumor deposits in rectal cancer: A monocentric series of 505 patients

Benoit

Svrcek

Creavin

et al. 2020

Journal of Surgical Oncology

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Background and Objectives: It has been suggested that tumor deposits (TDs) may have a worse prognosis in rectal cancer compared with colonic cancer. The aim of this study was to assess TDs prognosis in rectal cancer. Methods: Patients who underwent total mesorectum excision for rectal adenocarcinoma (2011-2016) were included. A case-matched analysis was performed to assess the accurate impact of TDs for each pN category after exclusion of synchronous metastasis. Results: A total of 505 patients were included. TDs were observed in 99 (19.6%) patients, (pN1c = 37 [7.3%]). TDs were associated with pT3-T4 stage (P = .037), synchronous metastasis (P = .003), lymph node (LN) invasion (P = .041), vascular invasion (P = .001), and perineural invasion (P < .001). TD was associated with a worse 3-year disease-free survival (DFS) among pN0 (51.2% vs 79.8%; P < .001); pN1 patients (35.2% vs 70.1%; P = .004) but not among pN2 patients (37.5% vs 44.7%; P = .499). After matching, pN1c patients had a worse 3-year DFS compared with pN0 patients (58.6% vs 82.4%; P = .035) and a tendency toward a worse DFS among N1 patients (40.1% vs 64.2%; P = .153). DFS was worse when one TD was compared with one invaded LN (40.8% vs 81.3%; P < .001). Conclusion: In rectal cancer, TDs have a metastatic risk comparable to a pN2 stage which may lead to changes in adjuvant treatment.

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Radiotherapy for Patients With Resected Tumor Deposit–Positive Colorectal Cancer: A Surveillance, Epidemiology, and End Results–Based Population Study

Cited by 15 publications

References 34 publications

Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Prognostic value of tumor deposits in rectal cancer: A monocentric series of 505 patients

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