External validation of a genitourinary cancer-specific prognostic scoring system to predict survival for patients with bone metastasis (modified B-FOM scoring model): Comparison with other scoring models in terms of accuracy

Owari, Takuya; Miyake, Makito; Nakai, Yasushi; Tanaka, Nobumichi; Itami, Yoshitaka; Hirao, Shuya; Momose, Hitoshi; Nakagawa, Yasuaki; Maesaka, Fumisato; Shimizu, Takuto; Iemura, Yusuke; Matsumoto, Yoshihiro; Kuwada, Masaomi; Otani, Takeshi; Otsuka, Kenji; Okajima, Eijiro; Hosokawa, Yukinari; Okamura, Ryosuke; Fujimoto, Kiyohide

doi:10.1016/j.jbo.2020.100344

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…In this organ-specific ORR study, the poor response to pembrolizumab of bone metastasis was prominent ( Figure 2 ). Bone metastases in UC have a poorer prognosis than those in other genitourinary cancers (prostate or renal cell cancer), as previously reported by our group [ 12 ]. Bone metastasis is an important condition that significantly impairs quality of life (QOL) when skeletal-related events (SREs) occur [ 13 ].…”

Section: Discussionsupporting

confidence: 77%

Organ-Specific and Mixed Responses to Pembrolizumab in Patients with Unresectable or Metastatic Urothelial Carcinoma: A Multicenter Retrospective Study

Shimizu

Miyake

Nishimura³

et al. 2022

Cancers

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To investigate the organ-specific response and clinical outcomes of mixed responses (MRs) to immune checkpoint inhibitors (ICIs) for unresectable or metastatic urothelial carcinoma (ur/mUC), we retrospectively analyzed 136 patients who received pembrolizumab. The total objective response rate (ORR) and organ-specific ORR were determined for each lesion according to the Response Evaluation Criteria in Solid Tumors version 1.1 as follows: (i) complete response (CR), (ii) partial response (PR), (iii) stable disease (SD), and (iv) progressive disease (PD). Most of the organ-specific ORR was 30–40%, but bone metastasis was only 5%. There was a significant difference in overall survival (OS) between responders and non-responders with locally advanced lesions and lymph node, lung, or liver metastases (HR 9.02 (3.63–22.4) p < 0.0001; HR 3.63 (1.97–6.69), p < 0.0001; HR 2.75 (1.35–5.59), p = 0.0053; and HR 3.17 (1.00–10.0), p = 0.049, respectively). MR was defined as occurring when PD happened in one lesion plus either CR or PR occurred in another lesion simultaneously, and 12 cases were applicable. MR was significantly associated with a poorer prognosis than that of the responder group (CR or PR; HR 0.09 (0.02–0.35), p = 0.004). Patients with bone metastases benefitted less. Care may be needed to treat patients with MR as well as patients with pure PD. Further studies should be conducted in the future.

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Section: Discussionsupporting

confidence: 77%

Organ-Specific and Mixed Responses to Pembrolizumab in Patients with Unresectable or Metastatic Urothelial Carcinoma: A Multicenter Retrospective Study

Shimizu

Miyake

Nishimura³

et al. 2022

Cancers

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“…To address this issue, several survival scoring systems were proposed to optimize the balance between surgical interventions and survival outcomes among patients with bone metastases. In 2021, for instance, Owari et al (26) The present study introduced six approaches to develop corresponding six models and used 13 metrics to assess and compare the prediction performance of these models. Subsequently, the optimal model was selected to present model explainability, and six individual specific cases were illustrated in the study.…”

Section: Discussionmentioning

confidence: 99%

“…To address this issue, several survival scoring systems were proposed to optimize the balance between surgical interventions and survival outcomes among patients with bone metastases. In 2021, for instance, Owari et al ( 26 ) proposed a scoring system to predict survival outcomes among patients with bone metastases in a cohort of 489 patients with genitourinary cancers; Katagiri et al ( 27 ) developed a new prognostic scoring system to predict survival outcome in a cohort of 808 patients with skeletal metastases; More recently, Chi et al ( 28 ) created a nomogram to predict survival prognosis among 326 bone metastases patients with head and neck cancer. However, the above scoring systems are derived from several primary cancer types which might have different patterns of metastases, and thus the prognostic ability might cannot appropriately work among bone metastases in particular lung cancer patients who had a relatively very short life expectancy.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients

Cui

Shi

Wang

et al. 2022

Front. Public Health

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PurposeBone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data.MethodsThis study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold.ResultsAmong all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807–0.833), 0.820 (95% CI: 0.807–0.833), and 0.815 (95% CI: 0.801–0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001).ConclusionsUsing machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

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“…Ряд прогностических факторов, таких как гистологическая структура опухоли, количество костных и висцеральных метастазов, используют все авторы, а другие, такие как индекс массы тела, безметастатический промежуток, индекс Чарлсона, применяют крайне редко [11,12]. В настоящее время существуют разнонаправленные тенденции в виде увеличения и усложнения существующих шкал и в виде их упрощения, иногда до двух критериев.…”

Section: Introductionunclassified

Development of a prognostic model in patients with metastatic bone lesions to choose surgical treatment: retrospective study

Bukharov¹,

Kurilchik²,

Barashev³

et al. 2023

J. Mod. Onco.

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Choosing surgical management for patients with metastatic bone lesions is one of the essential problems of modern oncology. Surgical interventions are aimed at palliative treatment in most patients with metastatic skeletal lesions. However, curative resections with reconstruction and plasty steps may be considered in selected cases of a solitary metastatic lesion. The life expectancy prognosis based on the histological structure of the tumor is a significant and decisive factor in choosing the appropriate surgery. Aim. To develop a prognostic model for choosing surgical treatment for metastatic bone lesions. Materials and methods. Treatment analysis of 715 patients with a history of surgery for metastatic bone lesions of various localizations is presented. A total of 780 surgical interventions were performed. Surgeries for the complications of bone metastases were mainly performed on the spine (48.5% of all surgeries), followed by long bones with 247 (35%) surgeries, pelvic bones with 81 (11%) interventions, and the chest wall with 40 (5.5%) surgeries. Results. The most unfavorable prognostic factors in patients with metastatic bone lesions are the histological type of the primary tumor of the rapid growth group (risk ratio [RR]=5.11), visceral metastases (RR=3.1), Charlson Comorbidity Index over 10 (RR=3.07) and presence of critical laboratory abnormalities (RR=2.91), as they have the highest rates of impact on survival (over 2.9). Conclusion. The developed 14-point mathematical score of life expectancy prognosis, which includes five oncological and four clinical factors, defines with an accuracy of 91% the risk groups of good (estimated life expectancy over one year), moderate (6 to 12 months), and poor (less than six months) prognosis in patients with metastatic bone lesions.

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External validation of a genitourinary cancer-specific prognostic scoring system to predict survival for patients with bone metastasis (modified B-FOM scoring model): Comparison with other scoring models in terms of accuracy

Cited by 13 publications

References 25 publications

Organ-Specific and Mixed Responses to Pembrolizumab in Patients with Unresectable or Metastatic Urothelial Carcinoma: A Multicenter Retrospective Study

Organ-Specific and Mixed Responses to Pembrolizumab in Patients with Unresectable or Metastatic Urothelial Carcinoma: A Multicenter Retrospective Study

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients

Development of a prognostic model in patients with metastatic bone lesions to choose surgical treatment: retrospective study

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