Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.
Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.
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DOI：
10.1001/jamanetworkopen.2020.25881
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发表时间：
2020-11-02
影响因子：
13.8
通讯作者：
Pearson AT
中科院分区：
文献类型：
--
作者：
Howard FM;Kochanny S;Koshy M;Spiotto M;Pearson AT
机器学习生存模型能否预测哪些头颈部鳞状细胞癌患者将从辅助化疗中受益？在这项对33名 526名患者的队列研究中，根据3种机器学习模型进行的治疗，使用国家癌症数据库进行训练和验证，与生存受益相关。这些模型建议在44%到52%的人群中进行化疗。这些发现表明，机器学习模型有可能更好地选择需要三模式治疗的中等风险患者，进一步的研究是有必要的。这项队列研究评估了机器学习模型是否可以识别将从化疗中受益的中等风险头颈部鳞状细胞癌患者。术后放化疗是切缘阳性或包膜外侵犯的癌症的标准治疗方法，但化疗对其他中等风险特征的患者的益处尚不清楚。评估机器学习模型是否可以识别中危头颈部鳞状细胞癌患者，这些患者将从化疗中受益。这项队列研究包括2004年1月1日至2016年12月31日期间被诊断为口腔、口咽、下咽或喉部鳞状细胞癌的患者。2例患者切除病变并接受辅助放射治疗。分析时间为2019年10月1日至2020年9月1日。患者是从国家癌症数据库中挑选出来的，这是一个以医院为基础的注册机构，收集了美国70%以上新诊断癌症的数据。使用80%的队列训练三个机器学习生存模型，其余20%用于评估模型性能。单独接受辅助化疗或放射治疗。接受机器学习模型推荐的治疗的患者与没有接受治疗的患者进行了比较。根据模型建议进行治疗的总存活率是主要结果。次要结果包括推荐化疗的频率和建议接受化疗的患者与单纯接受放疗的患者的化疗益处。共有33名 527名患者(24名 189名(72%)男性；28名 036名(84%)年龄在70岁的≤)符合纳入标准。验证数据集的中位随访期为43.2个月(四分位数范围19.8-65.5个月)。DeepSurv、神经多任务Logistic回归和生存森林模型分别推荐17例 589(52%)、15例 917(47%)和14例 912(44%)患者接受化疗。根据模型建议的治疗与生存收益相关，DeepSurv的风险比为0.79(95%CI，0.72-0.85；P <； .001)，神经多任务Logistic回归的风险比为0.83(95%CI，0.77-0.90；P <； .001)，随机存活森林模型的风险比为0.90(95%CI，0.83-0.98；P = .01)。对于被建议单独接受放射治疗的患者，没有看到化疗对生存的好处。这些发现表明，机器学习模型可以识别可以从化疗中受益的中等风险患者。这些模型预测，大约一半的此类患者没有从化疗中获得额外的好处。
Can machine learning survival models predict which patients with intermediate-risk head and neck squamous cell carcinoma would benefit from adjuvant chemotherapy? In this cohort study of 33 526 patients, treatment according to 3 machine learning models, trained and validated using the National Cancer Database, was associated with a survival benefit. These models recommended chemoradiation in 44% to 52% of the population. These findings suggest that machine learning models have the potential to better select intermediate-risk patients in need of trimodality therapy, and further study is warranted. This cohort study evaluates whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation. Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features. To evaluate whether machine learning models could identify patients with intermediate-risk head and neck squamous cell carcinoma who would benefit from chemoradiation. This cohort study included patients diagnosed with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx from January 1, 2004, through December 31, 2016. Patients had resected disease and underwent adjuvant radiotherapy. Analysis was performed from October 1, 2019, through September 1, 2020. Patients were selected from the National Cancer Database, a hospital-based registry that captures data from more than 70% of newly diagnosed cancers in the United States. Three machine learning survival models were trained using 80% of the cohort, with the remaining 20% used to assess model performance. Receipt of adjuvant chemoradiation or radiation alone. Patients who received treatment recommended by machine learning models were compared with those who did not. Overall survival for treatment according to model recommendations was the primary outcome. Secondary outcomes included frequency of recommendation for chemotherapy and chemotherapy benefit in patients recommended for chemoradiation vs radiation alone. A total of 33 527 patients (24 189 [72%] men; 28 036 [84%] aged ≤70 years) met the inclusion criteria. Median follow-up in the validation data set was 43.2 (interquartile range, 19.8-65.5) months. DeepSurv, neural multitask logistic regression, and survival forest models recommended chemoradiation for 17 589 (52%), 15 917 (47%), and 14 912 patients (44%), respectively. Treatment according to model recommendations was associated with a survival benefit, with a hazard ratio of 0.79 (95% CI, 0.72-0.85; P < .001) for DeepSurv, 0.83 (95% CI, 0.77-0.90; P < .001) for neural multitask logistic regression, and 0.90 (95% CI, 0.83-0.98; P = .01) for random survival forest models. No survival benefit for chemotherapy was seen for patients recommended to receive radiotherapy alone. These findings suggest that machine learning models may identify patients with intermediate risk who could benefit from chemoradiation. These models predicted that approximately half of such patients have no added benefit from chemotherapy.
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影响因子：
3.7
作者：
Bilimoria KY;Stewart AK;Winchester DP;Ko CY
通讯作者：
Ko CY
影响因子：
2
作者：
HARRELL, FE;LEE, KL;ROSATI, RA
通讯作者：
ROSATI, RA
影响因子：
158.5
作者：
Bernier, J;Domenge, C;van Glabbeke, M
通讯作者：
van Glabbeke, M
影响因子：
2.6
作者：
Giacalone, Nicholas J.;Qureshi, Muhammad M.;Minh Tam Truong
通讯作者：
Minh Tam Truong
DOI：
10.1002/hed.25103
发表时间：
2018-06-01
影响因子：
2.9
作者：
Osborn, Virginia Wedell;Givi, Babak;Schreiber, David
通讯作者：
Schreiber, David